Noise-Robust Feature Extraction Based on Forward Masking

Forward masking is a phenomenon of human auditory perception, that a weaker sound is masked by a preceding stronger masker. The actual cause of forward masking is not clear, but synaptic adaptation and temporal integration are heuristic explanations. In this paper, we postulate the mechanism of forward masking to be synaptic adaptation and temporal integration, and incorporate them in the feature extraction process of an automatic speech recognition system to improve noise-robustness. The synaptic adaptation is implemented by a high-pass filter, and the temporal integration is implemented by a bandpass filter. We apply both filters in the domain of log mel-spectrum. On the Aurora 3 tasks, we evaluate three modified mel-frequency cepstral coefficients: synaptic adaptation only, temporal integration only, and both synaptic adaptation and temporal integration. Experiments show that the overall improvement is 16.1%, 21.8%, and 26.2% respectively in the three cases over the baseline.