Classification Of Speech Segments Research Articles

Voiced/ Unvoiced Speech Discrimination Using Symbolic Dynamics

The aim of this study is to evaluate how far the nonlinear symbolic dynamics approach helps to characterize the nonlinear properties of speech and thereby discriminate between voiced and unvoiced speech segments. The symbolic dynamics calculations were performed on voiced speech, unvoiced speech and silence data. Differences were found in histogram properties and complexity measures of symbol sequences among the three groups. The results of the analysis suggest that the nonlinear symbolic dynamics approach is helpful in classification of speech segments.

Two-Feature Voiced/Unvoiced Classifier Using Wavelet Transform

This paper proposes a new wavelet-based algorithm for voice/unvoiced classification of speech segments. The classification process is based on: 1) statistical analysis of the energy-frequency distribution of the speech signal using wavelet transform, and 2) estimation of the short-time zero-crossing rate of the signal. First, the ratio of the average energy in the low-frequency wavelet subbands to that of highest-frequency wavelet subband is computed for each time segment of the pre-emphasised speech using a 4-level dyadic wavelet transform, and compared to a pre-determined threshold. This is followed by measuring the zero- crossing rate of the segment and comparing it to a threshold determined by a continually up-dated value of the median of the zero-crossing rates of the speech signal. An experimentally verified criterion based on the re- sults of the above two comparison processes is then applied to obtain the classification decision. The perform- ance of the algorithm has been evaluated on speech data taken from the TIMIT database, and is shown to yield high classification accuracy and robustness to additive noise. technique for V/UV speech classification using two features of the speech signal: (a) the frequency distribution of the average energy and (b) zero-crossing rate, for each speech segment. The performance of the proposed technique has been evaluated using a large database of both clean speech and speech degraded with additive noise. Following this introduction, the second Section gives an overview of the discrete wavelet transform (DWT). The de- tailed description of the proposed V/UV classification algo- rithm is given next. In the forth Section, the various experi- mental results of the performance evaluation of the algorithm are illustrated and discussed. The article concludes by giving a summary of the proposed algorithm and findings of the evaluation process.

Adaptive WRLS-VFF for speech analysis

The paper shows that an adaptive weighted recursive least squares algorithm with a variable forgetting factor (WRLS-VFF) will adjust the size of the data segment to be analyzed according to its time-varying characteristics, as during the transitions between vowels and consonants. The algorithm can accurately estimate the vocal tract formants, anti-formants, and their bandwidths, be used for glottal inverse filtering, perform voiced (V)/unvoiced (U)/silent (S) classification of speech segments, estimate the input excitation (either white noise or periodic pulse trains), and estimate the instant of glottal closure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A Bayesian-adaptive decision method for the V/UV/S classification of segments of a speech signal

In this correspondence, a method for voiced (V), unvoiced (UV), or silence (S) classification of speech segments, based on the maximum a posteriori probability criterion, is presented. The a posteriori probabilities of the three classes are determined using a vector x = ( f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,... , f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> ) of measurements on the segment under consideration. It is assumed that the vector x has an L-dimensional Gaussian distribution with an expected random value also characterized by an L-dimensional Gaussian distribution. In addition, it is assumed that the sequence of the classes constitutes a first-order stationary Markov chain. The initial parameters are estimated in a training phase. During the application phase, the decision method is adapted by using the previous classifications in order to update the probability density function (pdf) of the expected random values.