A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition

Abstract

Absb-act-In speech analysis, the voiced-unvoiced decision is usually performed in conjunction with pitch analysis. The linking of voicedunvoiced (V-UV) decision to pitch analysis not only results in unnecessary complexity, but makes it difficult to classify short speech segments which are less than a few pitch periods in duration. In this paper, we describe a pattern recognition approach for deciding whether a given segment of a speech signal should be classified as voiced speech, unvoiced speech, or silence, based on measurements made on the signal. In this method, five different measurements axe made on the speech segment to be classified. The measured parameters are the zero-crossing rate, the speech energy, the correlation between adjacent speech samples, the first predictor coefficient from a 12-pole linear predictive coding (LPC) analysis, and the energy in the prediction error. The speech segment is assigned to a particular class based on a minimumdistance rule obtained under the assumption that the measured parameters are distributed according to the multidimensional Gaussian probability density function. The means and covariances for the Gaussian distribution are determined from manually classified speech data included in a training set. The method has been found to provide reliable classification with speech segments as short as 10 ms and has been used for both speech analysis-synthesis and recognition applications. A simple nonlinear smoothing algorithm is described to provide a smooth 3-level contour of an utterance for use in speech recognition applications. Quantitative results and several examples illustrating the performance of the methOd are included in the paper.

A data-driven non-intrusive measure of speech quality and intelligibility

“…6) Zero crossing rate: The zero crossing rate has been successfully used as a feature for voiced-unvoiced speech and silence classification [53] and is also included here.…”

Techniques for Crosslingual Voice Conversion

“…Some of the techniques for deciding whether a given signal is voiced or unvoiced use measures such as frequency band ratios [19], zero crossing rates [4] and cepstrum coefficients [2].…”

Spectral Estimation of Speech Corrupted by Colored Noise

“…For the first stage, we can use the previous methods (e . g. [2,6,18] The block diagram of Figure 1 summarizes the whole procedure [17] . At the first step, a decision is made on the presence/absence of speech activity within the current frame of about 30 ms duration, which is the standard length of one analysis frame in speech analysis.…”