Automatic Speech Recognition for Sanskrit

This paper presents our work on building a speaker independent, large vocabulary continuous speech recognition system for Sanskrit using HMM Toolkit (HTK). To our knowledge, this is the maiden attempt on a Sanskrit automatic speech recognizer. A Sanskrit speech corpus with a vocabulary size of 8370 words is built. The corpus contains orthographic, phoneme and word level transcriptions of 1360 sentences. The speech data were collected from 3 sources: All India Radio website, Indian Heritage Group under C-DAC and Vyoma Linguistic Labs Foundation. Mel Frequency Cepstral Coefficients together with 0th order coefficient and delta and acceleration parameters are used as features. Triphone HMMs, trained using HTK, are used as acoustic model. Bigram probabilities with backoff smoothing are used as language model. Both phoneme and word level recognizers were developed on the Sanskrit corpus. The system provides a word level accuracy of 89.64% and a sentence level correctness of 58.76% on the test set of 274 sentences. A graphical user interface for the speech recognizer is built using Java Swings.