Pitch-Scaled Spectrum Based Excitation Model for HMM-based Speech Synthesis

摘要

The speech generated by hidden Markov model (HMM)-based speech synthesis systems (HTS) suffers from a 'buzzing' sound, which is due to an over-simplified vocoding technique. This paper proposes a new excitation model that uses a pitch-scaled spectrum for the parametric representation of speech in HTS. A residual signal produced using inverse filtering retains the detailed harmonic structure of speech that is not part of the linear prediction (LP) spectrum. By using pitch-scaled spectrums, we can compensate the LP spectrum using the detailed harmonic structure of the residual signal. This spectrum can be compressed using a periodic excitation parameter so that it can used to train HTS. We define an aperiodic measure as the harmonics-to-noise ratio, and calculate a voicing-cut off frequency to fit the aperiodic measure to a sigmoid function. We combine the LP coefficient, pitch-scaled spectrum, and sigmoid function to create a new parametric representation of speech. Listening tests were carried out to evaluate the effectiveness of the proposed technique. This vocoder received a mean opinion score of 4.0 in analysis-synthesis experiments, before dimensionality reduction. By integrating this vocoder into HTS, we improved the sound of the synthesized speech compared with the pulse train excitation model, and demonstrated an even better result than STRAIGHT-HTS.