Tamil Speech Recognition System Research Articles

Retraction Note: An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map

Retraction Note: An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map

RETRACTED ARTICLE: An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map

Speech recognition is one of the entrancing fields in the zone of computer science. Exactness of speech recognition framework may decrease because of the nearness of noise exhibited by the speech signal. Consequently, noise removal is a fundamental advance in automatic speech recognition (ASR) system. ASR is researched for various languages in light of the fact that every language has its particular highlights. Particularly, the requirement for ASR framework in Tamil language has been expanded broadly over the most recent couple of years. In this work, bidirectional recurrent neural network (BRNN) with self-organizing map (SOM)-based classification scheme is suggested for Tamil speech recognition. At first, the input speech signal is pre-prepared by utilizing Savitzky–Golay filter keeping in mind the end goal to evacuate the background noise and to improve the signal. At that point, Multivariate Autoregressive based highlights by presenting discrete cosine transformation piece to give a proficient signal investigation. And in addition, perceptual linear predictive coefficients likewise separated to enhance the classification accuracy. The feature vector is shifted in measure, for picking the right length of feature vector SOM utilized. At long last, Tamil digits and words are ordered by utilizing BRNN classifier where the settled length feature vector from SOM is given as input, named as BRNN-SOM. The experimental analysis demonstrates that the suggested conspire accomplished preferable outcomes looked at over exist deep neural network–hidden Markov model algorithm regarding signal-to-noise ratio, classification accuracy, and mean square error.

Performance enhancement of syllable based Tamil speech recognition system using time normalization and rate of speech

The automatic speech recognition (ASR) is an active field of research. The performance of the ASR can be degraded due to various features like environmental noise, channel distortion and speech rate variability. The speech rate variability is one of the important features that affect the accuracy of the speech recognition system (SRS). In this research work, the speech signal is categorized as slow, normal and fast speech using features like the sound intensity level, time duration and root mean square. This paper addresses the enhancement of the performance of a SRS by applying time normalization to the speech signal. The comparison of the proposed Model and baseline syllable based SRS is done.

Design of language models at various phases of Tamil speech recognition system

This paper describes the use of language models in various phases of Tamil speech recognition system for improving its performance. In this work, the language models are applied at various levels of speech recognition such as segmentation phase, recognition phase and the syllable and word level error correction phase. The speech signals were segmented at phonetic level based on their acoustic characteristics. The wrongly identified segmentation points were detected and corrected using articulatory feature based phoneme language model. The segmented signals were mapped to their phonemes. The ambiguities in the recognized phonemes were reduced by using inter and intra word based language models. The recognized phonemes were grouped together to form syllables and then words. The errors in the syllables and words were detected and corrected by using the syllable and morpheme based language models developed for Tamil language. The performance of the Tamil speech recognition system was improved by using the language models at different phases of speech recognition. Recognition rate of 74.11% was obtained by applying language models at segmentation phase, which was further improved to 84.11% at phoneme recognition phase and finally to 87.1% at syllable level and word level recognition phase. Thus the use of language models has drastically reduced the error rates at various levels and improved the recognition rate of Tamil speech recognition system. Keywords: Language model, articulatory features, morphemes, syllables.

Comparison of performance of enhanced morpheme-based language model with different word-based language models for improving the performance of Tamil speech recognition system

This paper describes a new technique of language modeling for a highly inflectional Dravidian language, Tamil. It aims to alleviate the main problems encountered in processing of Tamil language, like enormous vocabulary growth caused by the large number of different forms derived from one word. The size of the vocabulary was reduced by, decomposing the words into stems and endings and storing these sub word units (morphemes) in the vocabulary separately. A enhanced morpheme-based language model was designed for the inflectional language Tamil. The enhanced morpheme-based language model was trained on the decomposed corpus. The perplexity and Word Error Rate (WER) were obtained to check the efficiency of the model for Tamil speech recognition system. The results were compared with word-based bigram and trigram language models, distance based language model, dependency based language model and class based language model. From the results it was analyzed that the enhanced morpheme-based trigram model with Katz back-off smoothing effect improved the performance of the Tamil speech recognition system when compared to the word-based language models.

Time scale modification and vocal tract length normalization for improving the performance of Tamil speech recognition system implemented using language independent segmentation algorithm

This paper describes the work done in improving the performance of Tamil speech recognition system by using Time Scale Modification (TSM) and Vocal Tract Length Normalization (VTLN) techniques. The speech recognition system for Tamil language was developed using a new approach of text independent speech segmentation, with a phoneme based language model for recognition. There is degradation in the performance of speech recognition due to variations in the speaking rate and vocal tract shape among different speakers. In order to improve the performance of speech recognition system, both TSM and VTLN normalization techniques were used in this work. The TSM was implemented using the Phase vocoder approach and the VTLN was implemented using speaker specific bark/mel scale in bark/mel domain. The performance of Tamil speech recognition system was improved by performing both TSM and VTLN normalization techniques.