Speech enhancement using voice source models

TitleSpeech enhancement using voice source models
Publication TypeConference Paper
Year of Publication1999
AuthorsYasmin, A., P. Fieguth, and L. Deng
Conference Name25th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
KeywordsAR models, AR process, autoregressive models, autoregressive processes, excitation models, four-parameter LF model, human vocal tract, impulsive-driven AR, parameter estimation, performance, periodic voiced speech, speech enhancement, speech model, speech synthesis, voice source models, voiced phonemes, voicing, white noise, white-noise-driven AR

Autoregressive (AR) models have been shown to be effective models of the human vocal tract during voicing. However the most common model of speech for enhancement purposes, AR process excited by white noise, fails to capture the periodic nature of voiced speech. Speech synthesis researchers have long recognized this problem and have developed a variety of sophisticated excitation models, however these models have yet to make an impact in speech enhancement. We have chosen one of the most common excitation models, the four-parameter LF model of Fant, Liljencrants and Lin (1985), and applied it to the enhancement of individual voiced phonemes. Comparing the performance of the conventional white-noise-driven AR, an impulsive-driven AR, and AR based on the LF model shows that the LF model yields a substantial improvement, on the order of 1.3 dB