Estimation of Voicing-Character of Speech Spectra Based on Spectral Shape

This letter presents a method for estimation of the voicing-character of speech spectra. It is based on a calculation of a similarity between the shape of the signal short-term magnitude spectra and spectra of the frame-analysis window, which is weighted by the signal magnitude spectra. It is demonstrated that the proposed voicing measure is related to the local SNR of noise-corrupted voiced speech. The performance is evaluated for detection of voiced regions in the spectra of speech corrupted by various types of noise. The experimental results in terms of false-acceptance and false-rejection show errors of less than 5% for speech corrupted by white noise at the local SNR of 10 dB and in terms of recognition accuracy obtained by an ASR system using the voicing information estimated by the proposed method and by the full a priori knowledge about the noise show similar recognition performance