Voicing-Character Estimation of Speech Spectra: Application to Noise Robust Speech Recognition

This paper presents a novel method for estimating the voicing-character of speech spectra, demonstrates its employment in noise robust ASR and proposes a modified calculation of filter-bank energies. The proposed voicing-character estimation is based on calculation of a similarity between the shape of the signal short-term magnitude spectra around spectral peaks and spectra of the frame-analysis window. The similarity is weighted by the signal magnitude spectra to reflect the filter-bank analysis typically used in feature extraction for speech recognition. The experimental results show less than 5% false-acceptance and false-rejection errors in detection of voiced filter-bank channels in speech signal corrupted by white noise at 10 dB local SNR. The recognition results obtained by a missing-feature based ASR system using features estimated as voiced by the proposed method are very similar to using oracle voicing-label obtained by full a-priori knowledge of noise. The employment of features obtained by a modified calculation of filter-bank energies shows further improvements in the recognition accuracy