Dynamic Feature Extraction for Speech Signal Based On MUSIC and Modulation Spectrum

Abstract

A novel dynamic feature extraction algorithm is proposed to help improve speech recognition robustness in noise environmental conditions. Owing to the modulation spectrum having time-frequency agglomeration performance, according to different reflections in the modulation spectrum for interference and speech signals, we first calculate the Multiple Signal Classification (MUSIC) spectrum and then get the modulation spectrum. We then filter the modulation spectrum signal. For the filter signal, use a 32 frames signal is used as a processing unit to get the modulation spectrum energy vector. This reveals the close correlation between the speech signal frames and can well reflect speech dynamic characteristics. Finally, the cepstrum coefficients are extracted as the feature parameter. It not only adequately reflects speech dynamic characteristics, but also has lower sensitivity for the speech environment. The effectiveness of the feature is discussed in view of the class separability and speaker variability properties. We evaluated the feature under different kinds of noise (white noise, pink noise, street noise, and panzer noise) and different signal-to-noise ratios (-5 dB, 0 dB, 5 dB, 10 dB, and 15 dB). The experimental results show that the novel feature has good robustness and computational efficiency under low signal-to-noise ratios and plays a very good foreshadowing role in latter speech research.