Articulation based admissible wavelet packet feature based on human cochlear frequency response for TIMIT speech recognition

To deal with non-stationary and quasi-stationary signals, wavelet transform has been used as an effective tool for the time-frequency analysis. In the recent years, wavelet transform has been used extensively for feature extraction in noisy speech recognition. These filters have the benefit of having frequency bands spacing similar to the auditory Equivalent Rectangular Bandwidth (ERB) scale. Central frequencies of ERB are equally distributed with the frequency response of the human cochlea. This paper deals with the speaker-independent Automatic Speech Recognition (ASR) system for continuous speech. This Hidden Markov Model (HMM) based ASR system was developed for English using recordings of four regions taken from TIMIT database. A new set of features were derived using wavelet packet transform’s multi-resolution capabilities and having an advantage of ERB filter based on the human cochlea. New set of wavelet features have shown significant improvements in the noisy environment, especially at low SNR values.