HMM-based speech recognition using adaptive framing

A common approach in mapping a signal to discrete events is to define a set of symbols that correspond to useful acoustic features of the signal over a short constant time interval. This paper proposes a Hidden Markov Models (HMM) based speech recognition by using cepstrum feature of the signal over adaptive time interval. First pitch period is detected by dyadic wavelet transform and divides the voiced speech signal according to the detected period. Then, system performs HMM-based speech recognition using cepstrum feature to classify the speech signals. Two speech recognition systems have been developed, one is based on constant time framing and the other is adaptive framing. The results are compared and found that adaptive framing method shows better result in both data distribution and recognition rate.