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.
In the paper REF , authors were improved the accuracy of Tamil speech system by designing language models at various level such as segmentation phase, recognition phase and syllable and word level error correction phase.
Design of language models at various phases of Tamil speech recognition system
International journal of engineering science and technology
Computer Science