Speech conversion and its application to alaryngeal speech enhancement.
PublisherThe University of Arizona.
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AbstractIn this investigation, a vector quantization (VQ)-based speech conversion algorithm and a linear multivariate regression (LMR)-based speech conversion algorithm were modified, and the modified algorithms were applied to the enhancement of alaryngeal speech. The modifications were aimed at reducing the spectral distortion (bandwidth increase) in the VQ-based system and the spectral discontinuity in the LMR-based system. The spectral distortion in the VQ-based algorithm was compensated by formant enhancement using chirp z-transform and cepstral weighting. The spectral discontinuity in the LMR-based system was minimized by the use of overlapped subsets during the constructing of conversion mapping function. These modified algorithms were evaluated using simulated data and speech samples. Results of the evaluations indicated that the modified algorithms reduced conversion distortions. These modified algorithms were also used for the enhancement of alaryngeal speech. Results of perceptual evaluation indicated that listeners generally preferred to listen to the enhanced speech samples.
Degree ProgramSpeech and Hearing Sciences