DWS pitch detection algorithm extended to the time domain

The DWS pitch detection algorithm finds the best harmonic relation among the peaks in the amplitude spectrum of the acoustic signal. It is argued that, for high-pitch signals, there is relatively little information available in the spectrum compared to the amount of temporal information that can be obtained from the autocorrelation function, for instance. This paper describes how the DWS algorithm was applied to the autocorrelation function and how the results from the frequency and time domains were combined to obtain a more reliable pitch estimate

Nose catcher: A pick-up for nasal speech sounds

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Intonation contour generator

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Research tools for speech perception studies

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Robust signal selection for lineair prediction analysis of voiced speech

This paper investigates a weighted LPC analysis of voiced speech. In view of the speech production model, the weighting function is either chosen to be the short-time energy function of the preemphasized speech sample sequence with certain delays or is obtained by thresholding the short-time energy function. In this method, speech samples are selectively weighted based on how well they match the speech production model. Therefore, the estimates of the LPC coefficients obtained by this novel LPC analysis are more accurate than those obtained from the conventional LPC analysis. They are also less sensitive to the values of the fundamental frequency than conventional LPC

Measurement of pitch in speech : an implementation of Goldstein's theory of pitch perception

Recent developments in hearing theory have resulted in the rather general acceptance of the idea that the perception of pitch of complex sounds is the result of the psychological pattern recognition process. The pitch is supposedly mediated by the fundamental of the harmonic spectrum which fits the spectrum of the complex sound optimally. The problem of finding the pitch is then equivalent to finding the best harmonic match. Goldstein [J. Acoust. Soc. Am. 54, 1496-1516 (1973)] has described an objective procedure for finding the best fit for stimuli containing relatively few spectral components. He uses a maximum likelihood criterion. Application of this procedure to various data on the pitch of complex sounds yielded good results. This motivated our efforts to apply the pattern recognition theory of pitch to the problem of measuring pitch in speech. Although we were able to follow the main line of Goldstein's procedure, some essential changes had to be made. The most important is that in our implementation not all spectral components of the complex sound have to be classified as belonging to the harmonic pattern. We introduced a harmonics sieve to determine whether components are rejected or accepted at a candidate pitch. A simple criterion, based on the components accepted and rejected, led to the decision on which candidate pitch was to be finally selected. The performance and reliability of this psychoacoustically based pitch meter were tested in a LPC-vocoder system