Abstract—This paper proposes three novel noise robustness techniques for speech recognition based on discrete wavelet transform (DWT), which are wavelet filter cepstral coefficients(WFCCs), sub-band power normalization (SBPN), and low pass filtering plus zero interpolation (LFZI). According to our experiments, the proposed WFCC is found to provide a more robust c0 (the zeroth ceptral coefficient) for speech recognition, and with the proper integration of WFCCs and the conventional MFCCs, the resulting compound features can enhance the recognition accuracy. Second, the SBPN procedure is found to reduce the power mismatch within each modulation spectral sub-band, and thus to improve the recognition accuracy significantly. Finally, the third technique, LFZI, can reduce the storage space for speech features, while it is still helpful in speech recognition under noisy conditions.
Index Terms—Discrete wavelet transform, wavelet filter cepstral coefficients, sub-band power normalization, low pass filtering and zero interpolation, speech recognition.
Jeih-weih Hung, Hao-teng Fan and Syu-Siang Wang are with the Department of electrical engineering, national Chi Nan University, Taiwan(e-mail: firstname.lastname@example.org, email@example.com).
Cite: Jeih-Weih Hung, Hao-Teng Fan, and Syu-Siang Wang, " Several New DWT-Based Methods for Noise-Robust Speech Recognition ," International Journal of Innovation, Management and Technology vol. 3, no. 5, pp. 547-551, 2012.