LAUSR

In the last few years, the need for Continuous Speech Recognition system in Tamil language has been increased widely. In this research work, efficient Continuous Tamil Speech Recognition (CTSR) technique is proposed under non stationary noisy environments. This research work consists of two stages such as speech enhancement and modelling phase. In this, the modified Modulation Magnitude Estimation based Spectral Subtraction with Chi-Square Distribution based Noise Estimation (SS–NE) algorithm is proposed to enhance the noisy Tamil speech signal under various non-stationary noise environments. In order to extract the speech segments from the continuous speech, further the enhanced speech signal is segmented through the combination of short-time signal energy and spectral centroid features of the signal. In this work, 26 mel frequency cepstral coefficients per frame are found as optimal values and they are considered as acoustic feature vectors for each frame. In this research work, the Fuzzy C-Means (FCM) clustering is used in order to cluster the extracted feature vectors into discrete symbols. From the evaluation results, it is found that the optimal number of clusters ‘C’ as 5. Finally, Tamil speech from various speakers is recognized using Expectation Maximization Gaussian Mixture Model (EM-GMM) with 16 component densities under continuous measurements of labelled features from FCM clustering techniques in order to reduce the word error rate. From the simulated results, it is observed that the proposed FCM with EM-GMM model for CTSR improves the recognition accuracy from 1.2 to 4.4% when compared to the existing algorithms under different noisy environments by reducing the WER from 1.6 to 5.47%.