Novel stochastic properties of the short-time spectrum for unvoiced pronunciation modeling and synthesis

Stochastic property of speech signal is a fundamental research topic in speech analysis and processing. In this paper, multiple levels of randomness in speech signal are discussed, and the stochastic properties of unvoiced pronunciation are studied in detail, which has not received sufficient research attention before. The study is based on the signals of sustained unvoiced pronunciation captured in the experiments, for which the amplitude and phase values in the short-time spectrum are studied as random variables. The statistics of amplitude for each frequency component is studied individually, based on which a new property of “consistent standard deviation coefficient” is revealed for the amplitude spectrum of unvoiced pronunciation. The relationship between the amplitude probability distributions of different frequency components is further studied, which indicates that all the frequency components have a common prototype of amplitude probability distribution. As an adaptive and flexible probability distribution, the Weibull distribution is adopted to fit the expectation-normalized amplitude spectrum data. The phase distribution for the short-time spectrum is also studied, and the results show a uniform distribution. A synthesis method for unvoiced pronunciation is proposed based on the Weibull distribution of amplitude and uniform distribution of phase, which is implemented by STFT with artificially generated short-time spectrum with random amplitude and phase. The synthesis results have identical quality of auditory perception as the original pronunciation, and have similar autocorrelation as that of the original signal, which proves the effectiveness of the proposed stochastic model of short-time spectrum for unvoiced pronunciation.