2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-15.1
Paper Title NOISE LEVEL LIMITED SUB-MODELING FOR DIFFUSION PROBABILISTIC VOCODERS
Authors Takuma Okamoto, National Institute of Information and Communications Technology, Japan; Tomoki Toda, Nagoya University, Japan; Yoshinori Shiga, Hisashi Kawai, National Institute of Information and Communications Technology, Japan
SessionSPE-15: Speech Synthesis 3: Vocoder
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Speech Processing: [SPE-SYNT] Speech Synthesis and Generation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Although diffusion probabilistic vocoders WaveGrad and DiffWave can realize real-time high-fidelity speech synthesis with a simple loss function in training, all noise components with full noise level range are predicted by one model in all iterations. This paper proposes a simple but effective noise level limited sub-modeling framework for diffusion probabilistic vocoders as Sub-WaveGrad and Sub-DiffWave. In the proposed method, DiffWave conditioned on continuous noise level as WaveGrad and spectral enhancement post-filtering are also provided. The proposed Sub-WaveGrad and Sub-DiffWave models are realized by using 10 sub-models. These models are separately trained with different limited noise levels, and only necessary sub-models are used according to the noise schedule in inference. The results of experiments using a Japanese female speech corpus indicate that both the proposed Sub-WaveGrad and Sub-DiffWave outperform vanilla WaveGrad and DiffWave in terms of the model accuracy and synthesis quality while keeping the inference speed.