The ability of listeners to identify pairs of simultaneous synthetic vowels has been investigated in the first of a series of studies on the extraction of phonetic information from multiple‐talker waveforms. Both members of the vowel pair had the same onset and offset times and a constant fundamental frequency of 100 Hz. Listeners identified both vowels with an accuracy significantly greater than chance. The pattern of correct responses and confusions was similar for vowels generated by (a) cascade formant synthesis and (b) additive harmonic synthesis that replaced each of the lowest three formants with a single pair of harmonics of equal amplitude. In order to choose an appropriate model for describing listeners’ performance, four pattern‐matching procedures were evaluated. Each predicted the probability that (i) any individual vowel would be selected as one of the two responses, and (ii) any pair of vowels would be selected. These probabilities were estimated from measures of the similarities of the aud...

Modeling the perception of concurrent vowels: Vowels with the same fundamental frequency

Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using dynamic programming methods that minimize ad hoc cost functions. The goal of the current work is to provide both point estimates and associated uncertainties of center frequencies and bandwidths in a statistically principled state-space framework. Extended Kalman (K) algorithms take advantage of a linearized mapping to infer formant and antiformant parameters from frame-based estimates of autoregressive moving average (ARMA) cepstral coefficients. Error analysis of KARMA, wavesurfer, and praat is accomplished in the all-pole case using a manually marked formant database and synthesized speech waveforms. KARMA formant tracks exhibit lower overall root-mean-square error relative to the two benchmark algorithms with the ability to modify parameters in a controlled manner to trade off bias and variance. Antiformant tracking performance of KARMA is illustrated using synthesized and spoken nasal phonemes. The simultaneous tracking of uncertainty levels enables practitioners to recognize time-varying confidence in parameters of interest and adjust algorithmic settings accordingly.

Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking.

The autocorrelation and covariance methods of linear prediction are formulated in terms of an inverse digital filter in cascade form, rather than the traditional direct form, to allow pole locations in the system model to be readily estimated and constrained. Iterative solution of the corresponding nonlinear normal equations is described. Applications to speech analysis and the compensation of biomedical signals are briefly discussed.

Linear prediction in cascade form

Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using dynamic programming methods that minimize ad hoc cost functions. The goal of the current work is to provide both point estimates and associated uncertainties of center frequencies and bandwidths in a statistically principled state-space framework. Extended Kalman (K) algorithms take advantage of a linearized mapping to infer formant and antiformant parameters from frame-based estimates of autoregressive moving average (ARMA) cepstral coefficients. Error analysis of KARMA, WaveSurfer, and Praat is accomplished in the all-pole case using a manually marked formant database and synthesized speech waveforms. KARMA formant tracks exhibit lower overall root-mean-square error relative to the two benchmark algorithms, with third formant tracking more challenging. Antiformant tracking performance of KARMA is illustrated using synthesized and spoken nasal phonemes. The simultaneous tracking of uncertainty levels enables practitioners to recognize time-varying confidence in parameters of interest and adjust algorithmic settings accordingly.

KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking

Hearing impaired people have serious difficulties learning to produce intelligible speech. To facilitate speech training for hearing impaired people, many researchers have developed various speech training aids, such as F/sub 0/ (fundamental frequency) indicator, N (nasality) indicator, S (fricative /s/) indicator, and spectra displayer. However, most of these training aids treat only a single speech parameter. An integrated speech training system was implemented. The system makes it possible to display vocal tract shape, as well as other speech parameters, in real time. A self-training program for hearing impaired persons has been developed to help them correct errors. To estimate speech parameters, it is assumed that the speech production process is an autoregressive model. Using a linear predictive analysis, vocal tract lateral shapes and log spectra are estimated. Log energy of the speech signal is used as intensity. Fundamental frequency and nasality are also detected using a vibration sensor. To check the reliability of the developed system, parameters of normal people were tested and training tests for hearing impaired children were performed. The results showed that the authors' proposed system is very useful for speech training of the hearing impaired. >

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Integrated speech training system for hearing impaired

Three methods of extracting resonance information from predictor-coefficient coded speech are compared. The methods are finding roots of the polynomial in the denominator of the transfer function using Newton iteration, picking peaks in the spectrum of the transfer function, and picking peaks in the negative of the second derivative of the spectrum. A relationship was found between the bandwidth of a resonance and the magnitude of the second derivative peak. Data, accumulated from a total of about two minutes of running speech from both female and male talkers, are presented illustrating the relative effectiveness of each method in locating resonances. The second-derivative method was shown to locate about 98 percent of the significant resonances while the simple peak-picking method located about 85 percent.

E. Palmer

Papers

A comparison of three methods of extracting resonance information from predictor-coefficient coded speech