Wavelet Speech Enhancement Based on Time–Scale Adaptation Mohammed Bahoura a and Jean Rouat b,* a D´ epartement de math´ ematiques, d’informatique et de g´ enie Universit´ e du Qu´ ebec ` a Rimouski, 300 all´ ee des Ursulines, Rimouski, Qu´ ebec, Canada, G5L 3A1. b D´ epartement de g´ enie ´ electrique et g´ enie informatique Universit´ e de Sherbrooke, 2500 boulevard de l’Universit´ e, Sherbrooke, Qu´ ebec, Canada, J1K 2R1. Abstract We propose a new speech enhancement method based on time and scale adapta- tion of wavelet thresholds. The time dependency is introduced by approximating the Teager Energy of the wavelet coefficients, while the scale dependency is intro- duced by extending the principle of level dependent threshold to Wavelet Packet Thresholding. This technique does not require an explicit estimation of the noise level or of the apriori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MEL- scale decomposition based on wavelet packets is also compared to the common wavelet packet scale. Comparison in terms of Signal-to-Noise Ratio (SNR) is reported for time adapta- tion and time-scale adaptation thresholding of the wavelet coefficients thresholding. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models Speech recognition experiments are conducted on the AURORA–2 database and show that the proposed method improves the speech recognition rates for low SNRs. Key words: speech enhancement, wavelet transform, Teager energy operator, speech recognition. * Corresponding author. Tel: +1-819-821-8000 Email address: Jean.Rouat@usherbrooke.ca (Jean Rouat). Preprint submitted to Elsevier Science 31 May 2006