Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fundamentals of speech recognition
Fundamentals of speech recognition
Multirate Digital Signal Processing
Multirate Digital Signal Processing
Wavelet packets based features selection for voiceless plosives classification
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
IEEE Transactions on Audio, Speech, and Language Processing
A Robust Viterbi Algorithm Against Impulsive Noise With Application to Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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This article evaluates robustness of admissible wavelet packet based features for continuous speech recognition. The recognition accuracy is compared with the standard mel frequency cepstral coefficients (MFCC) under clean and noisy environment. This is carried out by adding white Gaussian and speech noise to the phonemes of the TIMIT database to generate different levels of signal to noise ratio. Further, a wavelet based denoising technique is proposed as a front-end for noise reduction. Soft and hard thresholding techniques are used with one-level and two-level wavelet based denoising. The speech recogniser with Continuous Density Hidden Markov's Model is used to model the phonemes for the word recognition task. The recognition performance achieved with denoising of the input speech shows improvement as compared to without denoising of both the MFCC and the wavelet based features.