Sumy Agrawal

In this paper, we study the performance of baseline hidden Markov model (HMM) for segmentation of speech signals. It is applied on single-speaker segmentation task, using Hindi speech database. The automatic phoneme segmentation framework evolved imitates the human phoneme segmentation process. A set of 44 Hindi phonemes were chosen for the segmentation experiment, wherein we used continuous density hidden Markov model (CDHMM) with a mixture of Gaussian distribution. The left-to-right topology with no skip states has been selected as it is effective in speech recognition due to its consistency with the natural way of articulating the spoken words. This system accepts speech utterances along with their orthographic “transcriptions” and generates segmentation information of the speech. This corpus was used to develop context-independent hidden Markov models (HMMs) for each of the Hindi phonemes. The system was trained using numerous sentences that are relevant to provide information to the passengers of the Metro Rail. The system was validated against a few manually segmented speech utterances. The evaluation of the experiments shows that the best performance is obtained by using a combination of two Gaussians mixtures and five HMM states. A category-wise phoneme error analysis has been performed, and the performance of the phonetic segmentation has been reported. The modeling of HMMs has been implemented using Microsoft Visual Studio 2005 (C++), and the system is designed to work on Windows operating system. The goal of this study is automatic segmentation of speech at phonetic level.

The prime objective of this paper is to conduct phoneme categorization experiments for Indian languages. In this direction a major effort has been made to categorize Hindi phonemes using a time delay neural network (TDNN), and compare the recognition scores with other languages. A total of six neural nets aimed at the major coarse of phonetic classes in Hindi were trained. Evaluation of each net on 350 training tokens and 40 test tokens revealed a 99% recognition rate for vowel classes, 87% for unvoiced stops, 82% for voiced stops, 94.7% for semi vowels, 98.1% for nasals and 96.4% for fricatives. A new feature vector normalisation technique has been proposed to improve the recognition scores