Feature extraction based on wavelet domain hidden markov tree model for robust speech recognition

Authors:
Sungyun Jung;Jongmok Son;Keunsung Bae
Affiliations:
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Korea;School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Korea;School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Korea
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 3
Cited 0

Acoustical and Environmental Robustness in Automatic Speech Recognition

Acoustical and Environmental Robustness in Automatic Speech Recognition
A vector Taylor series approach for environment-independent speech recognition

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IEEE Transactions on Signal Processing

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Abstract

We present a new feature extraction method for robust speech recognition in the presence of additive white Gaussian noise The proposed method is made up of two stages in cascade The first stage is denoising process based on the wavelet domain hidden Markov tree model, and the second one is reduction of the influence of the residual noise in the filter bank analysis To evaluate the performance of the proposed method, recognition experiments were carried out for noisy speech with signal-to-noise ratio from 25 dB to 0 dB Experiment results demonstrate the superiority of the proposed method to the conventional ones.