Inherent features of the Bangla (widely used as Bengali) language like long and short vowels and many instances of allophones make it difficult to build a continuous speech recognizer for the language. Stress and accent vary in spoken Bangla language from region to region. But in formal read Bangla speech, stress and accents are ignored. There are three approaches to continuous speech recognition (CSR) based on the sub-word unit viz. word, phoneme and syllable. Pronunciation of words and sentences are strictly governed by set of linguistic rules. Many attempts have been made to build continuous speech recognizers for Bangla for small and restricted tasks. However, medium and large vocabulary CSR for Bangla is relatively new and not explored. In this paper, the authors have attempted for extracting local features (LFs) from a Bangla input speech for tri-phone based automatic speech recognition (ASR) method. The method comprises two stages, where the first stage extracts LFs from input speech and the final stage generates word strings based on trip hone hidden Markov models (HMMs). The objective of this research is to build a medium vocabulary trip hone based continuous speech recognizer for Bangla language by extracting LFs over mel frequency cepstral coefficients (MFCCs). In this experimentation using Bangla speech corpus prepared by us, the recognizer provides higher word accuracy, word correct rate as well as sentence correct rate for trained and tested sentences with fewer mixture components in HMMs.
展开▼
