An investigation of multi-speaker training for wavenet vocoder
Tomoki Hayashi, Akira Tamamori, Kazuhiro Kobayashi, Kazuya Takeda, Tomoki Toda · 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · 2017
In this paper, we investigate the effectiveness of multi-speaker training for WaveNet vocoder. In our previous work, we have demonstrated that our proposed speaker-dependent (SD) WaveNet vocoder, which is trained with a single speaker's speech data, is capable of modeling temporal waveform structure, such as phase information, and makes it possible to generate more naturally sounding synthetic voices compared to conventional high-quality vocoder, STRAIGHT. However, it is still difficult to generate synthetic voices of various speakers using the SD-WaveNet due to its speaker-dependent property. Towards the development of speaker-independent WaveNet vocoder, we apply multi-speaker training techniques to the WaveNet vocoder and investigate its effectiveness. The experimental results demonstrate that 1) the multispeaker WaveNet vocoder still outperforms STRAIGHT in generating known speakers' voices but it is comparable to STRAIGHT in generating unknown speakers' voices, and 2) the multi-speaker training is effective for developing the WaveNet vocoder capable of speech modification.