Combining speech enhancement and auditory feature extraction for robust speech recognition
Speech Communication - Special issue on noise robust ASR
Short-time phase spectrum in speech processing: A review and some experimental results
Digital Signal Processing
Effects of instantaneous multiband dynamic compression on speech intelligibility
EURASIP Journal on Applied Signal Processing
Perceptual features for automatic speech recognition in noisy environments
Speech Communication
Recognizing Reverberant Speech Based on Amplitude and Frequency Modulation
IEICE - Transactions on Information and Systems
Distributed speech recognition of mandarin digits string
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
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Most of the features used by modern automatic speech recognition systems, such as mel-frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) coefficients, represent spectral envelope of the speech signal only. Nevertheless, phase or frequency modulation as represented in recent perceptual models of the peripheral auditory system might also contribute to speech decoding. Furthermore, such features can be complementary to the envelope features. This paper proposes a variety of features based on a linear auditory filterbank, the Gammatone filterbank. Envelope features are derived from the envelope of the subband filter outputs. Phase/frequency modulation is represented by the subband instantaneous frequency (IF) and is used explicitly by concatenating envelope-based and IF-based features or is used implicitly by IF-based frequency reassignment. Speech recognition experiments using a standard HMM-based recognizer under both clean training and multi-condition training are conducted on a Chinese mandarin digits corpus. The experimental results show that the proposed envelope and phase based features can improve recognition rates in clean and noisy conditions compared to the reference MFCC-based recognizer.