<p>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.</p>
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@68d39971 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@1bfeff7b Through org.apache.xalan.xsltc.dom.DOMAdapter@236f299; Conference Code:85090
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.