Enhancing the magnitude spectrum of speech features for robust speech recognition

[[abstract]]In this article, we present an effective compensation scheme to improve noise robustness for the spectra of speech signals. In this compensation scheme, called magnitude spectrum enhancement (MSE), a voice activity detection (VAD) process is performed on the frame sequence of the utterance. The magnitude spectra of non-speech frames are then reduced while those of speech frames are amplified. In experiments conducted on the Aurora-2 noisy digits database, MSE achieves an error reduction rate of nearly 42% relative to baseline processing. This method outperforms well-known spectral-domain speech enhancement techniques, including spectral subtraction (SS) and Wiener filtering (WF). In addition, the proposed MSE can be integrated with cepstral-domain robustness methods, such as mean and variance normalization (MVN) and histogram normalization (HEQ), to achieve further improvements in recognition accuracy under noise-corrupted environments.[[note]]SC

Enhancing the magnitude spectrum of speech features for robust speech recognition

[[abstract]]In this article, we present an effective compensation scheme to improve noise robustness for the spectra of speech signals. In this compensation scheme, called magnitude spectrum enhancement (MSE), a voice activity detection (VAD) process is performed on the frame sequence of the utterance. The magnitude spectra of non-speech frames are then reduced while those of speech frames are amplified. In experiments conducted on the Aurora-2 noisy digits database, MSE achieves an error reduction rate of nearly 42% relative to baseline processing. This method outperforms well-known spectral-domain speech enhancement techniques, including spectral subtraction (SS) and Wiener filtering (WF). In addition, the proposed MSE can be integrated with cepstral-domain robustness methods, such as mean and variance normalization (MVN) and histogram normalization (HEQ), to achieve further improvements in recognition accuracy under noise-corrupted environments.[[note]]SC

Enhancing the magnitude spectrum of speech features for robust speech recognition

[[abstract]]In this article, we present an effective compensation scheme to improve noise robustness for the spectra of speech signals. In this compensation scheme, called magnitude spectrum enhancement (MSE), a voice activity detection (VAD) process is performed on the frame sequence of the utterance. The magnitude spectra of non-speech frames are then reduced while those of speech frames are amplified. In experiments conducted on the Aurora-2 noisy digits database, MSE achieves an error reduction rate of nearly 42% relative to baseline processing. This method outperforms well-known spectral-domain speech enhancement techniques, including spectral subtraction (SS) and Wiener filtering (WF). In addition, the proposed MSE can be integrated with cepstral-domain robustness methods, such as mean and variance normalization (MVN) and histogram normalization (HEQ), to achieve further improvements in recognition accuracy under noise-corrupted environments.[[note]]SC