Isolated and continuous bangla speech recognition: implementation, performance and application perspective

Research on automatic speech recognition has been approach progressively since 1930 and the major advances are since 1980 with the introduction of the statistical modeling of speech with the key technology Hidden Markov Model (HMM) and the stochastic language model (B. H. Juang, 2005). However, the existing reported research works on Bangla speech recognition didn’t yet incorporate the HMM technique and language model. This paper presents two different type of Bangla speech recognition from the implementation, performance and application perspective. We used HMM technique for pattern classification and also incorporate stochastic language model with the system. At the signal preprocessing level we perform adaptive noise elimination and end point detection. Spectral feature vectors such as Mel Frequency Cepstral Coefficients (MFCC) with the addition of first and second order coefficients are extracted from each speech wave signal. HMM is used for pattern classification. The system is implemented using the Cambridge Hidden Markov Modeling Toolkit (HTK) (S. Young, 2001-2005).