Magnitude spectrum enhancement for robust speech recognition

In this paper, an effective compensation scheme for the spectra of speech signals is proposed in order to improve their noise robustness. In this compensation scheme, named magnitude spectrum enhancement (MSE), a voice activity detection (VAD) process is first processed for the frame sequence of the utterance, and then the magnitude spectra of non-speech frames are set to be small while those of speech frames are amplified. In experiments conducted on the Aurora-2 noisy digits database, MSE achieves a relative error reduction rate of nearly 50% from the baseline processing, which outperforms the well-known spectral-domain speech enhancement techniques, spectral subtraction (SS) and Wiener filtering (WF). In addition, the proposed MSE can be integrated with cepstral-domain robustness methods, like mean and variance normalization (MVN) and histogram normalization (HEQ), to achieve further improved recognition accuracy under noise-corrupted environments.