Robust speech recognition in noisy environments based on subband spectral centroid histograms

We investigate how dominant-frequency information can be used in speech feature extraction to increase the robustness of automatic speech recognition against additive background noise. First, we review several earlier proposed auditory-based feature extraction methods and argue that the use of dominant-frequency information might be one of the major reasons for their improved noise robustness. Furthermore, we propose a new feature extraction method, which combines subband power information with dominant subband frequency information in a simple and computationally efficient way. The proposed features are shown to be considerably more robust against additive background noise than standard mel-frequency cepstrum coefficients on two different recognition tasks. The performance improvement increased as we moved from a small-vocabulary isolated-word task to a medium-vocabulary continuous-speech task, where the proposed features also outperformed a computationally expensive auditory-based method. The greatest improvement was obtained for noise types characterized by a relatively flat spectral density.