BACKGROUND

Technical Field

The present disclosure relates to a technique for synthesizing a voice.

Description of Related Art

Various voice synthesis techniques for synthesizing a voice expressive of phonetic identifiers are known. For example, Non-Patent Document 1 discloses a technique for synthesizing a singing voice using a neural network. In the technique disclosed in Merlijn Blaauw, Jordi Bonada, “A NEURAL PARAMETRIC SINGING SYNTHESIZER,” arXiv, 12 Apr. 2017 (hereafter, “Non-Patent Document 1”), control data indicative of a pitch, a phonetic identifier, and a sound duration for each of notes of a song are input to the neural network, whereby a series of frequency spectra of harmonic components and a series of frequency spectra of non-harmonic components are generated. The frequency spectra of the harmonic components and the frequency spectra of the non-harmonic components are then synthesized, whereby a time-domain voice signal representing the waveform of the singing voice is generated.

The technique described in Non-Patent Document 1 has a drawback in that although by use of the technique it is possible to generate a natural sounding voice for harmonic components of a singing voice, it is difficult to generate a natural sounding voice for non-harmonic components, such as breathing components, fricative sounds, and so forth.

SUMMARY

In view of the above circumstances, it is an object of the present disclosure to generate a natural voice that is close to a real human voice both in harmonic components and non-harmonic components.

In one aspect, a voice synthesis method includes: supplying a first trained model with control data including phonetic identifier data to generate a series of frequency spectra of harmonic components; supplying a second trained model with the control data to generate a waveform signal representative of non-harmonic components; and generating a voice signal including the harmonic components and the non-harmonic components based on the series of frequency spectra of the harmonic components generated by the first trained model and the waveform signal representative of the non-harmonic components generated by the second trained model.

In another aspect, a voice synthesis apparatus includes: a first trained model configured to generate a series of frequency spectra of harmonic components according to control data including phonetic identifier data; a second trained model configured to generate a waveform signal representative of non-harmonic components according to the control data; and a synthesis processor configured to generate a voice signal including the harmonic components and the non-harmonic components based on the series of frequency spectra of the harmonic components generated by the first trained model and the waveform signal representative of the non-harmonic components generated by the second trained model.

In still another aspect, a non-transitory computer-readable recording medium has recorded thereon a computer program for causing a computer to perform a voice synthesis method of: supplying a first trained model with control data including phonetic identifier data to generate a series of frequency spectra of harmonic components; supplying a second trained model with the control data to generate a waveform signal representative of non-harmonic components; and generating a voice signal including the harmonic components and the non-harmonic components based on the series of frequency spectra of the harmonic components generated by the first trained model and the waveform signal representative of the non-harmonic components generated by the second trained model.

