A novel hybrid neuro-wavelet system for robust speech recognition

This paper presents a new automatic speech recognition system featuring the application of wavelet transform to speech enhancement method based on multilayer perceptron (MLP) classifier with a hidden Markov model (HMM). With the features extracted from a wavelet packet transform, different speech utterances are effectively discriminated by local discriminant bases. The extracted features is further processed by a feed-forward subsystem, a discriminant function minimum based blind adaptive filter for noise cancellation, and an unvoiced speech enhancement. A MLP network is used as the classifier before the Viterbi recognizer. Simulation results in adverse environments showed that the proposed system can achieve the best independent word recognition rate of 96.21%. The recognition degraded gracefully when it was tested by deliberately contaminating the signal with noises from the NOISEX-92 database