AN INVESTIGATION OF MULTI-SPEAKER TRAINING FORWAVENET VOCODER

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 multi-speaker 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.