Qualification: 
Ph.D
cs_kumar@cb.amrita.edu

Dr. Santhosh Kumar C. joined Amrita in 2001. He has been a visiting researcher to Faculty of Information Technology, Brno University of Technology, Czech Republic in 2007. In 2009, Dr. Santhosh served as a visiting researcher to School of Computer and Electrical Engineering, University of Auckland, New Zealand, and School of Electrical Engineering, University of New South Wales, Sydney, Australia. He is currently leading the research activities in Machine Intelligence Research Lab of Amrita School of Engineering. Dr. Santhosh's areas of research interests are Spoken Language Processing, Machine Fault Identification and Applications of Signal Processing to Biomedical Applications.

 

 

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

J. Rajevenceltha, Dr. Santhosh Kumar C., and A. Kumar, A., “Improving the performance of multi-parameter patient monitors using feature mapping and decision fusion”, in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, pp. 1515-1518.[Abstract]


Multi-parameter patient monitor (MPM) uses vital signs, heart rate, blood pressure, oxygen saturation (SpO2) and respiration rate to identify the condition of patients. In this work, we use a support vector machine (SVM) backend classifier with four vital signs as its input and experimented using different kernels. It was observed that the SVM with a radial basis function kernel (SVM-RBF) outperforms the other kernels. Compared to non-linear SVMs, the linear SVM is computationally more efficient. Therefore, in this work we explore the use of feature mapping using locality constrained linear coding (LLC) to linearize the input features and thereby enhancing the performance of MPMs with a linear SVM (LLC-linSVM). To improve the performance further, we normalized LLC features by l2-norm (nLLC-linSVM). A performance improvement of 0.53% and 0.96% absolute for overall classification accuracy and specificity respectively was obtained over the baseline SVM-RBF system. However, a deterioration in the sensitivity was noted. To take advantage of both SVM-RBF and nLLC-linSVM, we finally fused the decision scores of both the systems. The fusion weights were estimated empirically using a dataset which is used neither for training nor testing. After decision fusion, we achieved a performance improvement of 0.90% absolute for classification accuracy, 0.24% absolute for sensitivity and 1.12% absolute for specificity compared to the baseline. All the systems were compared using receiver operating characteristics (ROC) and the results show that the performance of the fused system is better than the individual systems. © 2016 IEEE. More »»

2017

Conference Paper

K. K. George, Das, R. K., Jelil, S., Das, K. A., Dr. Santhosh Kumar C., Prasanna, S. R. M., and Panda, A., “AMRITATCS-IITGUWAHATI combined system for the Speakers in the Wild (SITW) speaker recognition challenge”, in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, pp. 2842-2846.[Abstract]


In this work, the details of AMRITA-TCS and IITGUWAHATI speaker recognition systems submitted to the Speakers in the Wild (SITW) speaker recognition challenge are presented. The AMRITA-TCS system is a fusion of i-vector with a backend probabilistic linear discriminant analysis (i-PLDA) system and a cosine distance features (CDF) with backend support vector machine classifier (CDF-SVM) system, developed using the short term cepstral features, mel frequency cepstral coefficients (MFCC) and power normalized cepstral coefficients (PNCC), respectively. The IITGUWAHATI system is an i-PLDA system using MFCC with a vowel like region (VLR) based feature selection (i-PLDA-VLR). The experimental results reported in this work are based on the core-core condition of the challenge. Finally, a fusion of AMRITA-TCS and IITGUWAHATI speaker recognition systems is carried out that enhances the performance than each of the subsystems. © 2016 IEEE.

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2016

Conference Paper

N. R. Sujit, Dr. Santhosh Kumar C., and Rajesh, C. B., “Improving the performance of cardiac abnormality detection from PCG signal”, in AIP Conference Proceedings, 2016, vol. 1715.[Abstract]


The Phonocardiogram (PCG) signal contains important information about the condition of heart. Using PCG signal analysis prior recognition of coronary illness can be done. In this work, we developed a biomedical system for the detection of abnormality in heart and methods to enhance the performance of the system using SMOTE and AdaBoost technique have been presented. Time and frequency domain features extracted from the PCG signal is input to the system. The back-end classifier to the system developed is Decision Tree using CART (Classification and Regression Tree), with an overall classification accuracy of 78.33% and sensitivity (alarm accuracy) of 40%. Here sensitivity implies the precision obtained from classifying the abnormal heart sound, which is an essential parameter for a system. We further improve the performance of baseline system using SMOTE and AdaBoost algorithm. The proposed approach outperforms the baseline system by an absolute improvement in overall accuracy of 5% and sensitivity of 44.92%. © 2016 AIP Publishing LLC.

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2016

Conference Paper

K. A. Das, George, K. K., Dr. Santhosh Kumar C., S. Veni, and Panda, A., “Modified gammatone frequency cepstral coefficients to improve spoofing detection”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.[Abstract]


Voice spoofing is one of the major challenges that needs to be addressed in the development of robust speaker verification (SV) systems. Therefore, it is necessary to develop systems (spoofing detectors) that are able distinguish between genuine and spoofed speech utterances. In this work, we propose the use of modified gammatone frequency cepstral coefficients (MGFCC) on enhancing the performance of spoofing detection. We also compare the effectiveness of GMM based spoofing detectors developed using mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GFCC), modified group delay cepstral coefficients (MGDCC) and cosine normalized phase cepstral coefficients (CNPCC) with that of MGFCC. The experimental results on ASV spoof 2015 database show that MGFCC outperforms magnitude based, MFCC and GFCC, and phase based, MGDCC and CNPCC, features on the known attack conditions. Further, we performed a score level fusion of the systems developed using MFCC, MGFCC, MGDCC and CNPCC. It is observed that the fused system significantly outperforms all the individual systems for known and unknown attack conditions of ASV spoof 2015 database.

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2016

Conference Paper

P. G, P, V. Vardhan, Dr. Santhosh Kumar C., celtha, R., and A, P. Kumar K., “A comparative analysis of feature mapping techniques to enhance the performance of multiparameter patient monitoring systems”, in International Conference on Current Research Topics in Power, Nuclear, Fuel and Energy ( PNFE ) - 2016, 2016.

2016

Conference Paper

S. A. Kumar and Dr. Santhosh Kumar C., “Improving the intelligibility of dysarthric speech towards enhancing the effectiveness of speech therapy”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.[Abstract]


Dysarthria is a neuro-motor disorder in which the muscles used for speech production and articulation are severely affected. Dysarthric patients are characterized by slow or slurred speech that is difficult to understand. This work aims at enhancing the intelligibility of dysarthric speech towards developing an effective speech therapy tool. In this therapy tool, enhanced speech is used for providing auditory feedback with a delay to instill confidence in the patients, so that they can improve their speech intelligibility gradually through relearning. Feature level transformation techniques based on linear predictive coding (LPC) coefficient mapping and frequency warping of LPC poles are experimented in this work. Speech utterances from Nemours dataset with mild and moderate dysarthria are used to study the effectiveness of the proposed algorithms. The quality of the transformed speech is evaluated using subjective and objective measures. A significant improvement in the intelligibility of speech was observed. Our method henceforth could be used to enhance the effectiveness of speech therapy, by encouraging the dysarthric patients talk more, thus helping in their fast rehabilitation. More »»

2016

Conference Paper

A. Sathya, Swetha, J., Das, K. A., George, K. K., Dr. Santhosh Kumar C., and Aravinth J., “Robust features for spoofing detection”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.[Abstract]


It is very important to enhance the robustness of Automatic Speaker Verification (ASV) systems against spoofing attacks. One of the recent research efforts in this direction is to derive features that are robust against spoofed speech. In this work, we experiment with the use of Cosine Normalised Phase-based Cepstral Coefficients (CNPCC) as inputs to a Gaussian Mixture Model (GMM) back-end classifier and compare its results with systems developed using the popular short term cepstral features, Mel-Frequency Cepstral Coefficients (MFCC) and Power Normalised Cepstral Coefficients (PNCC), and show that CNPCC outperforms the other features. We then perform a score level fusion of the system developed using CNPCC with that of the systems using MFCC and PNCC to further enhance the performance. We use known attacks to train and optimise the system and unknown attacks to evaluate and present the results. More »»

2016

Conference Paper

K. K. George, Dr. Santhosh Kumar C., I, R. K., and Panda, A., “Improving Robustness of Speaker Verification Against Mimicked Speech”, in Odyssey 2016, 2016.[Abstract]


Making speaker verification (SV) systems robust to spoofed/mimicked speech attacks is very important to make its use effective in security applications. In this work, we show that using a proximal support vector machine backend classifier with i-vectors as inputs (i-PSVM) can help improve the performance of SV systems for mimicked speech as non-target trials. We compared our results with the state-of-the-art baseline i-vector with cosine distance scoring (i-CDS), i-vector with a backend SVM classifier (i-SVM) and cosine distance features with an SVM backend classifier (CDF-SVM) systems. In iPSVM, proximity of the test utterance to the target and nontarget class is the criteria for decision making while in i-SVM, the distance from the separating hyperplane is the criteria for the decision. It was seen that the i-PSVM approach is advantageous when tested with mimicked speech as non-target trials. This highlights that proximity to the target speakers is a better criteria for speaker verification for mimicked speech. Further, we note that weighting the target and non-target class examples helps us further fine tune the performance of i-PSVM. We then devised a strategy for estimating the weights for every example based on its cosine distance similarity with respect to the centroid of target class examples. The final i-PSVM with example based weighting scheme achieved an improvement of 3.39% absolute in EER when compared to the best baseline system, iSVM. Subsequently, we fused the i-PSVM and i-SVM systems and results show that the performance of the combined system is better than the individual systems. More »»

2015

Conference Paper

K. Ka George, Dr. Santhosh Kumar C., Panda, Ab, Raji Ramachandran, Das, K. Aa, and S. Veni, “Minimizing the false alarm probability of speaker verification systems for mimicked speech”, in 2015 International Conference on Computing and Network Communications, CoCoNet 2015, 2015, pp. 703-709.[Abstract]


Speaker verification (SV) systems need to be robust to mimicked voices of target speakers as non-target trials to make them usable in critical applications. However, the performance of SV systems for mimicked voice test conditions has not been extensively explored. © 2015 IEEE.

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2015

Conference Paper

K. Ta Sreekumar, Gopinath, Ra, Pushparajan, Mb, Raghunath, A. Sa, Dr. Santhosh Kumar C., Ramachandran, K. Ia, and Saimurugan, Mb, “Locality constrained linear coding for fault diagnosis of rotating machines using vibration analysis”, in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 2015.[Abstract]


Support Vector Machine (SVM) is an important machine learning algorithm widely used for the development of machine fault diagnosis systems. In this work, we use an SVM back-end classifier, with statistical features in time and frequency domain as its input, for the development of a fault diagnosis system for a rotating machine. Our baseline system is evaluated for its speed dependent and speed independent performances. In this paper, we use locality constrained linear coding (LLC) to map the input feature vectors to a higher dimensional linear space, and remove some of the speed specific dimensions to improve the speed independent performance of the fault diagnosis system. We use LLC to do the feature mapping to the higher dimensional space, and select only the k nearest neighbour basis vectors to represent the input feature vector and thus reduce/minimize the effect of speed specific factors from the input feature vector, and thus improve the speed independent performance of the fault diagnosis. We compare the performance of the LLC-SVM system for the time and frequency domain statistical features. The proposed approach has improved the overall classification accuracy by 11.81% absolute for time domain features and 10.53% absolute for frequency domain features compared to the baseline speed independent system. © 2015 IEEE.

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2015

Conference Paper

S. S. N. Bose and Dr. Santhosh Kumar C., “Improving the performance of continuous non-invasive estimation of blood pressure using ECG and PPG”, in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 2015.[Abstract]


Blood pressure (BP) is one of the vital signs for assessing the cardiovascular health condition of a person. In recent years, the continuous non-invasive monitoring of BP is of great interest in routine and critical bedside monitoring. Previous studies have shown that pulse transit time (PTT), time taken by the pressure wave to travel between two arterial locations can be a potential indicator of the BP changes. However, hemodynamic factors (HDF) and regulatory factors (RF) influence the changes in BP. In this paper, we propose a model considering the PTT, hemodynamic and regulatory factors derived from Photoplethysmogram (PPG) and Electrocardiogram (ECG) for the better estimation of BP compared to the baseline model using PTT alone. All experiments in this work were performed using ECG, PPG, Arterial Blood Pressure waveforms from Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) II database. BP was estimated using linear regression and its coefficients were calculated for each subject. In comparison to the baseline system using PTT alone, the system using PTT, HDF and RF as the input parameters achieves a reduction in the mean absolute error (MAE) and the root mean square error (RMSE) by 6.36% and 4.98% absolute for systolic BP and 12.28% and 28.23% absolute respectively for diastolic BP. The results suggest that the quality of BP estimation using PTT, HDF and RF is improved compared to the baseline system using PTT alone. © 2015 IEEE.

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2015

Conference Paper

Dr. Santhosh Kumar C., George, K. Ka, Ramachandran, K. Ia, and Panda, Ab, “Weighted cosine distance features for speaker verification”, in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 2015.[Abstract]


Cosine distance similarities with a set of reference speakers, cosine distance features (CDF), with a backend support vector machine classifier (CDF-SVM) have been explored in our earlier studies for improving the performance of speaker verification systems. Subsequently, we also investigated on its effectiveness in improving the noise robustness of speaker verification systems. In this work, we study how the performance of CDF-SVM systems can be further improved by weighting the feature vectors using latent semantic information (LSI) technique. We use mel frequency cepstral coefficients (MFCC), power normalized cepstral coefficients (PNCC), or delta spectral cepstral coefficients (DSCC) for deriving CDF. Experimental results on the female part of short2-short3 trials of NIST speaker recognition evaluation dataset show that the proposed weighted CDF-SVM system outperforms the baseline i-vector with cosine distance scoring (i-CDS), i-vector with a backend SVM classifier (i-SVM) and CDF-SVM systems. Finally, we fused the weighted CDF-SVM with i-CDS and the performance of the combined system was evaluated under different stationary and non-stationary additive noise test conditions. It was seen that the noise robustness of the fused weighted CDF-SVM+i-CDS system is significantly better than the individual systems and the fused CDF-SVM+i-CDS of our earlier work in both clean and noisy test environments except for the zero SNR level condition of certain noises. © 2015 IEEE.

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2015

Conference Paper

K. K. George, Dr. Santhosh Kumar C., Ramachandran, K. I., and Panda, A., “Cosine Distance Features for Robust Speaker Verification”, in Interspeech 2015, Dresden, Germany, 2015.[Abstract]


We use similarities with people we know already as a means to enhance the speaker verification accuracy. Motivated by this, we use cosine distance similarities with a set of reference speakers, cosine distance features (CDF), to improve the performance of speaker verification systems for clean and additive noise test conditions. We used mel frequency cepstral coefficients, power normalized cepstral coefficients, or delta spectral cepstral coef- ficients for deriving CDF. We then input CDF to a support vector machine (SVM) backend classifier (CDF-SVM). The performance of CDF-SVM was then compared with an i-vector with cosine distance scoring (i-CDS), and an i-vector with a backend SVM classifier (i-SVM) for stationary and non-stationary noises at different signal to noise ratio (SNR) levels. The experimental results show that, the CDF-SVM outperforms all other systems at high SNR and clean environments. However, in certain low SNR cases, i-CDS was found to be better. Finally, we fused the CDF-SVM with i-CDS and results show that the noise robustness of the combined system is significantly better than the individual systems for both high and low SNR levels. Index Terms: speaker verification, i-vectors, support vector machines, cosine distance features, noise robustness More »»

2015

Conference Paper

Dr. Santhosh Kumar C., “Fine Tuning Machine Fault Diagnosis System Towards Mission Critical Applications”, in International Symposium on Intelligent Systems Technologies and Applications (ISTA-2015), Kochi; 2015, 2015, pp. 217–226.

2015

Conference Paper

D. Mohan and Dr. Santhosh Kumar C., “A Low Cost Implementation of Multi-parameter Patient Monitor Using Intersection Kernel Support Vector Machine Classifier”, in 2nd International Conference on Communication Systems, 2015, Pilani;; 10/2015, 2015.[Abstract]


Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heartrate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board. More »»

2014

Conference Paper

K. T. Sreekumar, George, K. K., Arunraj, K., and Dr. Santhosh Kumar C., “Spectral matching based voice activity detector for improved speaker recognition”, in 2014 International Conference on Power Signals Control and Computations (EPSCICON), 2014.[Abstract]


For spoken language processing applications like speaker recognition/verification, not only that the silence segments do not contribute any speaker specific information, but also it dilutes the already available information content in the speech segments in the audio data. It has been experimentally studied that removing silence segments with the help of a voice activity detector(VAD) from the utterance before feature extraction enhances the performance of speaker recognition systems. Empirical algorithms using signal energy and spectral centroid(ESC) is one of the most popular approaches to VAD. In this paper, we show that using spectral matching (SM) to distinguish between silence and speech segments for VAD outperforms the VAD using ESC. We use a neural network with TempoRAl PatternS (TRAPS) of critical band energies as its input for improved performance. We evaluate the performance of VADs using a speaker recognition system developed for 20 speakers. More »»

2014

Conference Paper

K. Vishnuprasad, Dr. Santhosh Kumar C., Ramachandran, K. I., Vaijeyanthi, V., and Kumar, A. A., “Towards Building Low Cost Multi-Parameter Patient Monitors”, in Conference: International Conference on Communication and Computing (ICC- 2014), Bangalore, 2014.[Abstract]


Multi-parameter patient monitors (MPM) using human vital parameters, heart rate, blood pressure, respiration rate and oxygen saturation (SpO2), are extremely valuable in enhancing the health care of the patients in the intensive care unit (ICU) and general wards. Linear support vector machine (SVM) based implementations are computationally more efficient over the non-linear Kernel based SVMs. This makes the linear SVM implementation of patient monitors as a step towards building low cost affordable healthcare solutions, using smart phones or FPGAs. However, it may be noted that performance of non-linear Kernel SVM based MPM outperform linear SVM based MPMs. Hence, it would be of great interest to explore on how to enhance the performance of MPM systems using linear SVMs. There exists an intrinsic relationship between vital parameters that is known very well in the medical community, but not so well in the engineering community. In this work, we propose to use correlation features to capture the intrinsic relationship between the vital parameters through the geometric mean of the vital parameters taken in pairs of two, in addition to four vital parameters. The new features are seen to enhance the performance of the linear SVM classifier. We then implement the proposed algorithm in FPGA, to make a low cost implementation of the multi-parameter patient monitor possible. More »»

2014

Conference Paper

K. K. George, Arunraj, K., Sreekumar, K. T., Dr. Santhosh Kumar C., and Ramachandran, K. I., “Towards improving the performance of text/language independent speaker recognition systems”, in 2014 International Conference on Power Signals Control and Computations (EPSCICON), 2014.[Abstract]


Speaker Recognition is an active area of research for the last few decades for its applications in several national security, and other forensic applications. In this work, we present the details of a speaker recognition system developed using universal background model and support vector machines(UBM-SVM). We explored several techniques to improve the performance of the baseline system developed using mel frequency cepstral coefficients(MFCC) as input features. We developed and tested the speaker recognition system for 200 speakers, using the data collected over 13 different channels, such as handset regular phone, speaker phone, regular phone headphone, regular phone, etc. We experimented with the use of RelAtive SpecTrA (RASTA) processing, and feature warping on the input MFCC features, and nuisance attribute projection (NAP) on the Gaussian mixture model supervectors derived in the system. It was seen that these techniques have helped improve the system performance significantly by minimizing the effect of different channels on the system performance. The details of the system implementation and results are presented in this paper. The complete system is developed in MATLAB and C/C++. More »»

2014

Conference Paper

S. V. Thambi, Sreekumar, K. T., Dr. Santhosh Kumar C., and Raj, P. C. R., “Random forest algorithm for improving the performance of speech/non-speech detection”, in 2014 First International Conference on Computational Systems and Communications (ICCSC), 2014.[Abstract]


Speech/non-speech detection (SND) distinguishes between speech and non-speech segments in recorded audio and video documents. SND systems can help reduce the storage space required when only speech segments from the audio documents are required, for example content analysis, spoken language identification, etc. In this work, we experimented with the use of time domain, frequency domain and cepstral domain features for short time frames of 20 ms. size along with their mean and standard deviation for segments of size 200 ms. We then analysed if selecting a subset of the features can help improve the performance of the SND system. Towards this, we experimented with different feature selection algorithms, and observed that correlation based feature selection gave the best results. Further, we experimented with different decision tree classification algorithms, and note that random forest algorithm outperformed other decision tree algorithms. We further improved the SND system performance by smoothing the decisions over 5 segments of 200 ms. each. Our baseline system has 272 features, a classification accuracy of 94.45 % and the final system with 8 features has a classification accuracy of 97.80 %. More »»

2013

Conference Paper

R. Gopinath, Nambiar, T. N. P., Abhishek, S., Pramodh, S. M., Pushparajan, M., Ramachandran, K. I., Dr. Santhosh Kumar C., and Thirugnanam, R., “Fault injection capable synchronous generator for condition based maintenance”, in 7th International Conference on Intelligent Systems and Control, ISCO 2013, Coimbatore, Tamilnadu, 2013, pp. 60-64.[Abstract]


This paper presents the design specifications of an experimental setup capable of injecting faults in a synchronous generator to develop and test algorithms for condition based maintenance of aerospace applications. A 3 kVA alternator is designed to inject faults in the stator winding and field windings. The system is capable of injecting open and short circuit faults in the stator and rotor windings using a fault injection unit and record current, voltage and vibration signals from the respective sensors, with programming capability. The response of the synchronous generator during normal condition and faulty condition are discussed. The work reported in this paper will help other researchers develop low cost experimental facility to pursue research in machine condition monitoring. © 2013 IEEE.

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2010

Conference Paper

Dr. Santhosh Kumar C., Ambikairajah, Eb, Nosratighods, Mb, and Li, Hc, “Enhancing phone recognition accuracy using probabilistic features”, in APSIPA ASC 2010 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Biopolis, 2010, pp. 458-461.[Abstract]


In this paper, we study how performance of hidden Markov model - neural network (HMM-NN) phone recognizers can be enhanced using probabilistic features, without actually increasing the number of nodes in the neural network. This is necessary when the amount of labeled data available for training the models is small. We conduct two studies. One is to explore a multilingual probabilistic feature frontend. Another is to develop a multilingual acoustic model. We got an improvement of 2.87 and 4.75 per cent for Hindi and Tamil absolute phone recognition accuracy, and 3.03 and 7.02 per cent improvement for the multilingual phone recognition system for the respective languages.

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2010

Conference Paper

Dr. Santhosh Kumar C., Li, Hb, Tong, Rbc, Matějka, Pd, Burget, Ld, and Černocký, Jd, “Tuning phone decoders for language identification”, in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Dallas, TX, 2010, pp. 5010-5013.[Abstract]


Phonotactic approach, phone recognition to be followed by language modeling, is one of the most popular approaches to language identification (LID). In this work, we explore how language identification accuracy of a phone decoder can be enhanced by varying acoustic resolution of the phone decoder, and subsequently how multiresolution versions of the same decoder can be integrated to improve the LID accuracy. We use mutual information to select the optimum set of phones for a specific acoustic resolution. Further, we propose strategies for building multilingual systems suitable for LID applications, and subsequently fine tune these systems to enhance the overall accuracy. ©2010 IEEE.

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2005

Conference Paper

Dr. Santhosh Kumar C., Mohandas, V. P., and Li, H., “Multilingual Speech Recognition: A Unified Approach”, in InterSpeech, 2005.[Abstract]


In this paper, we present a unified approach for hidden markov model based multilingual speech recognition. The proposed approach could be used across acoustically similar as well as diverse languages. We use an automatic phone mapping algorithm to map phones across languages and reduce the effective number of phones in the multililingual acoustic model. We experimentally verify the effectiveness of the approach using two acoustically similar languages, Tamil and Hindi and also American English which is very different from the other two languages acoustically. The experimental results are very encouraging and demonstrate the effectiveness of the approach in building a universal multilingual speech recognition system. More »»

2004

Conference Paper

N. Udhyakumar, Dr. Santhosh Kumar C., Srinivasan, R., and Swaminathan, R., “Decision tree learning for automatic grapheme-to-phoneme conversion for Tamil”, in 9th Conference Speech and Computer, 2004.

2004

Conference Paper

Dr. Santhosh Kumar C., Udhyakumar, N., Srinivasan, R., and Shunmugom, C., “Automatic Grapheme to Phone Converter for Tamil using Rules”, in Oriental COCOSDA, 2004.

2004

Conference Paper

Dr. Santhosh Kumar C. and Li, H., “Language identification for multilingual speech recognition systems”, in 9th Conference Speech and Computer, 2004.[Abstract]


This paper describes the details of a language identification technique suitable for multilingual speech recognition systems. The present system is trained for two of the most widely spoken Indian languages, Tamil and Hindi. The technique could be extended for any number of languages without any substantial increase in the size of the multilingual speech recognition system More »»

2003

Conference Paper

Dr. Santhosh Kumar C. and Wei, F. Say, “A bilingual speech recognition system for English and Tamil”, in Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint, 2003.[Abstract]


This paper describes the details of a bilingual speech recognition system, AmritaRec, developed for English and Tamil. The performance results of the system is compared with that of a monolingual English speech recognition system adapted to Tamil using cross language transfer and cross language adaptation techniques. More »»

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

P. V. Sunil Nag, Silla, G. K., Gummadi, V. H. V., Harishankar, C. B., Ray, V. K., and Dr. Santhosh Kumar C., “Model Based Fault Diagnosis Of Low Earth Orbiting (LEO) Satellite Using Spherical Unscented Kalman Filter”, IFAC-PapersOnLine, vol. 49, pp. 635-638, 2016.[Abstract]


Model based fault detection and diagnosis (FDD) using a non-linear estimation technique is presented here. The non-linear estimation technique namely spherical Unscented Kalman Filter (UKF) has been applied to other kinds of estimation problems but has never been applied to the FDD problem of a Low Earth Orbiting (LEO) satellite. It has been shown in this work that compared to the standard UKF, which is a derivative free estimation technique unlike the popular Extended Kalman Filter (EKF), the spherical UKF can perform better in terms of computational savings without sacrificing accuracy. Hence it is better suited for real-time fault diagnosis. A planar model of the satellite is used to demonstrate the technique. © 2016 IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. More »»

2016

Journal Article

V. Krishna, P. Chendur, P., Abhilash, P. P., Abraham, R. T., Gopinath, R., and Dr. Santhosh Kumar C., “Improving the performance of wavelet based machine fault diagnosis system using locality constrained linear coding”, Advances in Intelligent Systems and Computing, vol. 530, pp. 951-964, 2016.[Abstract]


Support Vector Machine (SVM) is a popular machine learning algorithm used widely in the field of machine fault diagnosis. In this paper, we experiment with SVM kernels to diagnose the inter turn short circuit faults in a 3kVA synchronous generator. We extract wavelet features from the current signals captured from the synchronous generator. From the experiments, it is observed that the performance of baseline system is not satisfactory because of the inherent non linear characteristic of the features. Feature transformation techniques such as Principal Component Analysis (PCA) and Locality-constrained Linear Coding (LLC) are experimented to improve the performance of the baseline system. Although PCA allows for choosing dimensions with maximum variance, the dimension reduction always contributes to underperformance. On the other hand, LLC uses a codebook of basis vectors to map the features onto higher dimensional space where a computationally efficient linear kernel can be used. Experiments and results reveal that LLC outperforms PCA by improving the baseline system with an overall accuracy of 25.87 %, 21.47 %, and 21.79 % for the R, Y, and B phase faults respectively. © Springer International Publishing AG 2016.

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2016

Journal Article

R. Gopinath, Dr. Santhosh Kumar C., Ramachandran, K. I., Upendranath, V., and Kiran, P. V. R. Sai, “Intelligent fault diagnosis of synchronous generators”, Expert Systems with Applications, vol. 45, pp. 142 - 149, 2016.[Abstract]


Condition based maintenance (CBM) requires continuous monitoring of mechanical/electrical signals and various operating conditions of the machine to provide maintenance decisions. However, for expensive complex systems (e.g. aerospace), inducing faults and capturing the intelligence about the system is not possible. This necessitates to have a small working model (SWM) to learn about faults and capture the intelligence about the system, and then scale up the fault models to monitor the condition of the complex/prototype system, without ever injecting faults in the prototype system. We refer to this approach as scalable fault models. We check the effectiveness of the proposed approach using a 3 kVA synchronous generator as \{SWM\} and a 5 kVA synchronous generator as the prototype system. In this work, we identify and remove the system-dependent features using a nuisance attribute projection (NAP) algorithm to model a system-independent feature space to make the features robust across the two different capacity synchronous generators. The frequency domain statistical features are extracted from the current signals of the synchronous generators. Classification and regression tree (CART) is used as a back-end classifier. \{NAP\} improves the performance of the baseline system by 2.05%, 5.94%, and 9.55% for the R, Y, and B phase faults respectively. More »»

2016

Journal Article

R. Gopinath, Dr. Santhosh Kumar C., and RAMACHANDRAN, K. I., “Scalable Fault Models for Diagnosis of Synchronous Generators”, International Journal of Intelligent Systems Technologies and Applications, 2016.

2016

Journal Article

K. K. George, Das, R. Kumar, Jelil, S., K Das, A., Dr. Santhosh Kumar C., Prasanna, S. R. Mahadeva, and Panda, A., “AMRITATCS-IITGUWAHATI Combined System for the Speakers in the Wild (SITW) Speaker Recognition Challenge”, IEEE TENCON 2016, vol. 1, 2016.

2015

Journal Article

K. Ka George, Dr. Santhosh Kumar C., Dr. K. I. Ramachandran, and Panda, Ab, “Cosine distance features for improved speaker verification”, Electronics Letters, vol. 51, pp. 939-941, 2015.[Abstract]


Similarities are used with people known already as a means to enhance speaker verification accuracy. Motivated by this, experimental work has been conducted regarding the use of cosine distance (CD) similarity with respect to a set of reference speakers, CD features, with a back-end support vector machine (CDF-SVM) classifier for speaker verification. A state-of-the-art i-vector with CD scoring (i-CDS) is used as the baseline system for the experiments and for the computation of CD similarity. Experimental results on the telephone speech of the core short2-short3 conditions of NIST 2008 speaker recognition evaluation (SRE), for female, male and both-gender trials, show that the proposed CDF-SVM outperforms the baseline i-CDS system. The CDF-SVM achieved an absolute improvement of 1.16% in equal error rate (EER) and 0.38% in minimum DCF over the baseline i-CDS for female trials. Similar performance improvements were also obtained for the male and all-gender trials of the SRE. Finally, fusing the CDF-SVM with i-CDS gave the best overall performance, an absolute improvement of 4.19% in EER and 1.99% in minimum DCF, over the individual CDF-SVM system performance for the all-gender trials. Similar performance improvements were also achieved for male and female trials. © The Institution of Engineering and Technology 2015.

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2015

Journal Article

D. Mohan and Dr. Santhosh Kumar C., “An efficient IKSVM based multi-parameter patient monitoring system”, International Journal of Applied Engineering Research, vol. 10, pp. 22703-22710, 2015.[Abstract]


Multi-parameter patient monitors (MPMs) are extensively used for enhancing the quality of healthcare in both intensive care units (ICU) and in-patient wards. MPMs make use of the vital signs, respiration rate, heart rate, blood pressure and oxygen saturation (SpO<inf>2</inf>), for predicting the condition of patients. Support vector machine (SVM) is one of the most popularly used classification algorithms for developing MPMs. The kernel function, used in an SVM is a measure of similarity between any two examples, either belonging to same class or different classes. The selection of the kernel is an important aspect for the optimization of the system using SVM. If two patients have heart rates of 60 bpm and 80 bpm, intuition suggests that their heart rate similarity is 60 bpm. Extending this to n features, we may say that the total similarity is a summation of the individual similarities over n features, suggesting that intersection kernel is an ideal choice for MPM. In this paper, we explore the effectiveness of using intersection kernel SVM (IKSVM) for improving the performance of MPMs. We also compare the performance improvement of the MPM using IKSVM with the popularly used linear, polynomial and radial basis function (RBF) kernel MPMs. The results suggest that the use of intersection kernel can help enhance the performance of the MPMs significantly. Using IKSVM system, we obtained an improvement of 2.74%absolute for overall accuracy, 1.86% absolute for sensitivity and 3.00% absolute for specificity over the best baseline MPM using RBF kernel. © Research India Publications.

More »»

2015

Journal Article

B. K. Vaisakh, Gopinath, R., Dr. Santhosh Kumar C., and Ganesan, M., “Condition monitoring of synchronous generators using sparse coding”, International Journal of Applied Engineering Research, vol. 10, pp. 26689-26697, 2015.[Abstract]


This paper presents an efficient approach for condition based maintenance (CBM) of three phase synchronous generators for diagnosing inter-turn faults using current signatures. Support vector machines (SVM) are one of the widely used algorithms for the purpose of decision making in CBM. To improve the performance of the classifier, either we can select the kernel according to the features or select the features according to the kernel or linearize the features into a higher dimensional space and use linear SVM. In this work, we experiment with the third approach for improving the performance of the system. Sparse coding is an effective feature mapping technique that can be used to linearize the features into a higher dimensional space. Sparse coding improves the performance from 58.19% to 91.14% for R phase fault and from 67.64% to 91.50% forY phase fault and 73.02% to 94.79% for B phase faults respectively. © Research India Publications

More »»

2015

Journal Article

Dr. Santhosh Kumar C., K.I., R., and A.A., K., “Vital sign normalisation for improving performance of multi-parameter patient monitors”, Electronics Letters, vol. 51, pp. 2089-2090, 2015.[Abstract]


Using covariance normalisation (CVN) of vital signs is explored to improve the performance of multi-parameter patient monitors with heart rate, arterial blood pressure, respiration rate, and oxygen saturation (SpO2) as its input. The baseline system for the experiments is a support vector machine classifier with a radial basis function kernel. Although an improvement in the overall classification accuracy with the use of CVN is obtained, there was a deterioration in sensitivity. Furthermore, it is noted that the estimate of the covariance is often noisy, and therefore the covariance estimates is smoothed to obtain a performance improvement of 0.23% absolute for sensitivity, 1.34% absolute for specificity, and 1.08% absolute for the overall classification accuracy. Multi-parameter intelligent monitoring in intensive care II database for all the experiments is used.

More »»

2015

Journal Article

R. Gopinath, Dr. Santhosh Kumar C., Vishnuprasad, K., and RAMACHANDRAN, K. I., “Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator”, International Journal of Prognostics and Health Management, vol. 6, 2015.[Abstract]


Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline system is not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively. More »»

2015

Journal Article

K. K. George, Dr. Santhosh Kumar C., Sreekumar, K. T., K Das, A., Thottupattu, A. J., Kumar, M. S., and Ramachandran, K. I., “Amrita SRE Database: A Database for Evaluating Speaker Recognition Systems with Mimicked Speech”, 2015.[Abstract]


Evaluating speaker recognition systems under challenging conditions with mimicked voices of target speakers as non-target trials is very important in making decisions of its effectiveness and deployability in critical applications. The lack of a standard database is one of the bottlenecks in pursuing research in this direction. In this paper, we present the details of a new multilingual speaker recognition evaluation database, Amrita SRE database, in which the impersonators mimic the voices of target speakers. Our database consists of 115 target speakers and 76 impersonators speaking in six different languages: English, Hindi, Malayalam, Kannada, Tamil or Telugu, makes a total of 815 target speaker models, 6994 target trials and 3976 non-target trials. We evaluate the performance of different state-of-the-art speaker recognition systems using the normal target and nontarget trials and compared its performance with non-target trials replaced with mimicked voices. For all the systems, an average performance deterioration of 10% absolute in equal error rate (EER) was observed when tested with mimicked non-target utterances. More »»

2015

Journal Article

P. Jayaprakash, George, K. K., and Dr. Santhosh Kumar C., “An i-vector Based Speaker Recognition System With Improved Performance”, International Journal of Applied Engineering Research, vol. 10, pp. 30649–30657, 2015.

2015

Journal Article

D. D and Dr. Santhosh Kumar C., “Spoken Language Identification System for Web Audio and Videos”, International journal of Applied Engineering Research, vol. 10, pp. 32799–32802, 2015.

2014

Journal Article

, ,, Suraj, M., Pooja, J., Neeraj, R., and Dr. Santhosh Kumar C., “Drug Interaction Guide”, International Journal of Applied Engineering Research, vol. 9, no. 21, pp. 9529-9538, 2014.

2014

Journal Article

V. Vaijeyanthi, Vishnuprasad, K., Dr. Santhosh Kumar C., Ramachandran, K. I., Gopinath, R., A Kumar, A., and Yadav, P. Kumar, “Towards enhancing the performance of multi-parameter patient monitors”, Healthcare technology letters, vol. 1, p. 19, 2014.[Abstract]


Multi-parameter patient monitors (MPMs) have become increasingly important in providing quality healthcare to patients. It is well known in the medical community that there exists an intrinsic relationship between different vital parameters in a healthy person, these include heart rate, blood pressure, respiration rate and oxygen saturation. For example, an increase in blood pressure would lead to a decrease in the heart rate, and vice versa. Although it is likely to improve the performance of MPM systems, this fact is not explored in engineering research. In this work, experiments show that deriving additional features to capture the intrinsic relationship between the vital parameters, the alarm accuracy (sensitivity), no-alarm accuracy (specificity) and the overall performance of MPMs can be improved. The geometric mean of the product of all the vital parameters taken in pairs of two was used to capture the intrinsic relationship between the different parameters. An improvement of 10.55% for sensitivity, 0.32% for specificity and an overall performance improvement of 1.03% was obtained, compared to the baseline system using classification and regression tree with the four vital parameters. More »»

2014

Journal Article

A. Vaishnavi, B Raju, C., Prathiksha, G., L Reddy, H., and Dr. Santhosh Kumar C., “Comparison of Two Speaker Recognition Systems”, International Journal of Engineering and Advanced Technology (IJEAT), vol. 3, 2014.[Abstract]


This paper presents a comparison between two speaker recognition systems. One system uses 30 Shannon entropy values extracted from a four level wavelet packet decomposition method in addition to the first three formant frequencies as features and a cascaded feed forward back propagation neural network is used as classifier. The second system uses Mel frequency cepstral coefficients (MFCC) as features and a support vector machine (SVM) as classifier. Results suggest that wavelet based system has better performance than the classic MFCCs with an efficiency of 89.56%. More »»

2013

Journal Article

Va Vaijeyanthi, Dr. Santhosh Kumar C., Dr. K. I. Ramachandran, Joy, J. Ka, and Kumar, A. Ab, “Application-Specific fine tuning of multi-Parameter patient monitors”, Electronics Letters, vol. 49, pp. 1432-1433, 2013.[Abstract]


Multi-parameter patient monitors (MPMs) have become increasingly important in providing quality health care to patients. A high alarm accuracy (sensitivity) will need a lower threshold for alarm detection which will lead to lower no-alarm accuracy (specificity) and viceversa. MPMs when used in an intensive care unit (ICU) need to have high sensitivity. However they need to have high specificity when used in in-patient wards for regular health check-ups. Proposed is a novel algorithm to trade-off specificity for sensitivity and viceversa depending on the application. The proposed method is referred as detection error trade-off, trade-off specificity for better sensitivity and vice-versa. The algorithm will help to extend the application of MPMs from ICUs to in-patient wards and thus enhance the quality of health care. Experiments have been conducted with an MPM using the classification and regression tree algorithm. By using the proposed algorithm, an improvement of 10.18% in sensitivity was obtained by trading-off 0.40% in specificity. Furthermore, the overall performance of the refined system is 1.15% better than the baseline system. © The Institution of Engineering and Technology 2013.

More »»

2013

Journal Article

K. K. George and Dr. Santhosh Kumar C., “Towards enhancing the acoustic models for dysarthric speech”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8025 LNCS, pp. 183-188, 2013.[Abstract]


Dysarthria is a set of congenital and traumatic neuromotor disorders that impair the physical production of speech. These impairments reduce or remove the normal control of the vocal articulators. The acoustic characteristics of dysarthric speech is very different from the speech signal collected from a normative population, with relatively larger intra-speaker inconsistencies in the temporal dynamics of the dysarthric speech [1] [2]. These inconsistencies result in poor audible quality for the dysarthric speech, and in low phone/speech recognition accuracy. Further, collecting and labeling the dysarthric speech is extremely difficult considering the small number of people with these disorders, and the difficulty in labeling the database due to the poor quality of the speech. Hence, it would be of great interest to explore on how to improve the efficiency of the acoustic models built on small dysarthric speech databases such as Nemours [3], or use speech databases collected from a normative population to build acoustic models for dysarthric speakers. In this work, we explore the latter approach. © 2013 Springer-Verlag.

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2013

Journal Article

D. Das, Dr. Santhosh Kumar C., and Raj, P. C. Reghu, “Dysarthric Speech Enhancement using Formant Trajectory Refinement”, International Journal of Latest Trends in Engineering and Technology, vol. 2, pp. 88–92, 2013.[Abstract]


Dysarthria is a motor-neuro disorder that affects the quality of articulation required to produce speech. Also, there are temporal inconsistencies in the speech produced by people with dysarthria, leading to inconsistent formant trajectories. The trajectories change slowly in dysarthric speech, when compared to normal speech. In this work, we refine the formant trajectories of the dysarthric speech to improve its intelligibility. We use the P.563, P.862, standards along with composite measures to evaluate the quality of speech, before and after the refinement; we used NEMOURS database for the experiments involving mild dysarthria. For the proposed work, we attempt to emphasize the fast variations in the formant trajectories to enhance speech quality. It was observed that the quality of speech improved significantly. Our method will therefore encourage the dysarthric people to communicate more effectively and improve the pace of their rehabilitation. To the best of our knowledge, this type of work is not reported elsewhere. More »»

2013

Journal Article

S. S. Nair, Rechitha, C. R., and Dr. Santhosh Kumar C., “Rule-Based Grapheme to Phoneme Converter for Malayalam”, International Journal of Computational Linguistics and Natural Language Processing, vol. 2, 2013.[Abstract]


Speech is the primary means for communication. Since persons wish to interact with computers via speech, there have been a great number of efforts to incorporate speech into human-computer communication environments. Grapheme to Phoneme (G2P) has an inevitable role in speech synthesis as well as speech recognition systems. Grapheme to Phoneme conversion is the process of assigning phonetic transcription to words. This paper proposes a rule based G2P for Malayalam. Malayalam is one of the most prominent regional languages of Indian subcontinent, belonging to Dravidian family of languages. Grapheme is the smallest linguistic unit in a written language, which includes alphabets, digits and other symbols. Phoneme is the basic linguistic unit in spoken language. More »»

2013

Journal Article

Dr. Santhosh Kumar C., Kumar, R., and Das, D., “Analysis of Soliton Interaction in Optical Fiber Communication”, International Journal of Scientific Research in Inventions and New Ideas, vol. 1, p. 15, 2013.

2011

Journal Article

Dr. Santhosh Kumar C. and Mohandas, V. P., “Robust features for multilingual acoustic modeling”, International Journal of Speech Technology, vol. 14, pp. 147-155, 2011.[Abstract]


In this paper, we propose a technique to derive robust features for multilingual acoustic modeling using hidden Markov model-Gaussian mixture models (HMM-GMM). We achieve this by discriminatively combining the phonetic contexts of the target languages (languages in the multilingual system). Phonetic context is captured using wide temporal context of the features, and the dimensionality of the resulting feature set is reduced to suit the HMM-GMM implementation using a neural network with a bottle-neck in one of the hidden layers. The output before the non-linearity at the bottle-neck layer of the neural network is the new feature. Since the features are optimized for the target languages in the multilingual recognizer, they are referred to as Target Languages Oriented Features (TLOF). We perform our experiments for two of the most widely spoken Indian languages, Hindi and Tamil. TLOF offers significant performance improvements over both monolingual and multilingual phone recognizers using Mel frequency cepstral coefficients (MFCC). This emphasizes that TLOF can help share data across languages. It was also seen that TLOF can enhance the performance of monolingual acoustic models, compared to systems using MFCC. © 2011 Springer Science+Business Media, LLC.

More »»

2010

Journal Article

Dr. Santhosh Kumar C. and Mohandas, V. P., “Keyword Spotting in Multilingual Environments”, International Journal of Computer and Electrical Engineering, vol. 2, p. 1025, 2010.[Abstract]


Multilingual keyword spotting is of immense interest in the Indian context with as many as 30 languages spoken across the country, and more than one language spoken in most cities. In this paper, we present the details of a gender independent multilingual keyword spotting system developed using lattices generated by a multilingual phone decoder for two of the most widely spoken Indian languages, Hindi and Tamil. For building the multilingual phone decoder, we used phonetic as well as acoustic similarities to map phones across the two languages, and see that the approach offers promising results. A distance measure based on Kullback-Leibler divergence is used for measuring the acoustic similarity of phones. We used a hybrid hidden Markov model – neural network implementation of the phone decoder for all our experiments reported in this work. More »»

1988

Journal Article

Dr. Santhosh Kumar C., “Comments on "Subband coding of images”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, pp. 1089-1090, 1988.[Abstract]


The commenter feels that an equation in the above named work by J.W. Woods and S. O'Neil (ibid., vol. ASSP-34, pp. 1278-1288, Oct. 1986) is incorrect as given. A corrected version is provided along with another equation, which together allow one to obtain another equation as originally stated. The analysis following the equation remains valid.<> More »»

1987

Journal Article

Dr. Santhosh Kumar C., “Comments on "Quadrature mirror filter design for an arbitrary number of equal bandwidth channels"”, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 35, pp. 1642-1643, 1987.

Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2014

Conference Proceedings

S. Premanand, A. Kumar, A., and Dr. Santhosh Kumar C., “Improving the performance of multi-parameter patient monitor system by using additional features”, Proceedings of International Conference on Computation, Intelligence: Health and Disease, vol. 6. Springer briefs in Applied Science and Technology, pp. 53-58, 2014.

2014

Conference Proceedings

A. Anto, C, R. Raj P., Dr. Santhosh Kumar C., and T, S. K., “Towards Improving the Performance of Language Identification System for Indian Languages”, International Conference on Computational Systems and Communication. pp. 42-46, 2014.

2014

Conference Proceedings

N. Johnson, C, R. Raj P., Dr. Santhosh Kumar C., and George, K. K., “Towards improving the performance of speaker recognition systems”, International Conference on Computational Systems and Communication. pp. 38-41, 2014.

2006

Conference Proceedings

Dr. Santhosh Kumar C., Dr. Govind D., C., N., and Narwaria, M., “Grapheme to Phone Conversion for Hindi Oriental”, COCOSDA. Penang, Malaysia, 2006.

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