S. Lalitha currently serves as Assistant Professor(Sr.Gr) at department of Electronics and Communication,Amrita School of Engineering. 


Publication Type: Conference Paper

Year of Publication Title


S. Lalitha and Dr. Deepa Gupta, “An Encapsulation of Vital Non-Linear Frequency Features for Speech Applications”, in International Conference on Intelligent Computing (ICIC) 2018, Amrita School of Engineering, Bengaluru, 2018.


M. P Krishna, R Reddy, P., Narayanan, V., Lalitha, S., and Dr. Deepa Gupta, “Affective state recognition using audio cues”, in International Symposium on Intelligent Systems Technologies and Applications (ISTA 2018), PES Institute of Technology, Bengaluru, South Campus, India, 2018.


S. Lalitha, Mudupu, A., Nandyala, B. V., and Munagala, R., “Speech emotion recognition using DWT”, in 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015, 2015.[Abstract]

Emotion recognition from speech helps us in improving the effectiveness of human-machine interaction. This paper presents a method to identify suitable features in DWT domain and improve good accuracy. In this work, 7 emotions (Berlin Database) are recognized using Support Vector Machine (SVM) classifier. Entropy of Teager Energy operated Discrete Wavelet Transform (DWT) coefficients, Linear Predictive Cepstral Coefficients(LPCC), Mel Energy Spectral Dynamic Coefficients(MEDC), Zero Crossing Rate (ZCR), shimmer, spectral roll off, spectral flux, spectral centroid, pitch, short time energy and Harmonic to Noise Ratio (HNR) are considered as features. The obtained average accuracy is 82.14 % Earlier work done on emotion recognition using DWT coefficients yielded an accuracy of 63.63 % and 68.5% for 4 emotions on Berlin and Malayalam databases respectively. The proposed algorithm shows a significant increase in accuracy of about 15% to 20% for 7 emotions on Berlin database. Also, 100% efficiency has been achieved for four emotions with Simple Logistic classifier of WEKA 3.6 tool. © 2015 IEEE.

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