Delta-spectral cepstral coefficients for robust speech recognition

Almost all current automatic speech recognition (ASR) systems conventionally append delta and double-delta cepstral features to static cepstral features. In this work we describe a modified feature-extraction procedure in which the time-difference operation is performed in the spectral domain, rather than the cepstral domain as is generally presently done. We argue that this approach based on "delta-spectral" features is needed because even though delta-cepstral features capture dynamic speech information and generally greatly improve ASR recognition accuracy, they are not robust to noise and reverberation. We support the validity of the delta-spectral approach both with observations about the modulation spectrum of speech and noise, and with objective experiments that document the benefit that the delta-spectral approach brings to a variety of currently popular feature extraction algorithms. We found that the use of delta-spectral features, rather than the more traditional delta-cepstral features, improves the effective SNR by between 5 and 8 dB for background music and white noise, and recognition accuracy in reverberant environments is improved as well.