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.

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This paper presents an extensive study of zero crossings with peak amplitudes (ZCPA) features, that have earlier been shown to outperform both conventional and auditory-based features in presence of additive noise. The study starts by optimizing different parameters involved in ZCPA feature computation, followed by a comparison of ZCPA and MFCC features on two recognition tasks in different background conditions. The main differences between the two feature types were identified, and their individual effects on ASR performance were evaluated. The importance of a proper choice of analysis frame lengths and filter bandwidths in ZCPA feature extraction was demonstrated. Furthermore, the use of dominant frequency information in ZCPA features was found to be a major reason for increased robustness of ZCPA features compared to MFCC features.

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