RawNet: Fast End-to-End Neural Vocoder

• URL: http://arxiv.org/abs/1904.05351v2
• Date: Fri, 10 Mar 2023 03:22:22 GMT
• Title: RawNet: Fast End-to-End Neural Vocoder
• Authors: Yunchao He, Yujun Wang
• Abstract summary: RawNet is a complete end-to-end neural vocoder based on the auto-encoder structure for speaker-dependent and -independent speech synthesis. It automatically learns to extract features and recover audio using neural networks, which include a coder network to capture a higher representation of the input audio and an autoregressive voder network to restore the audio in a sample-by-sample manner.
• Score: 4.507860128918788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network-based vocoders have recently demonstrated the powerful ability to synthesize high-quality speech. These models usually generate samples by conditioning on spectral features, such as Mel-spectrogram and fundamental frequency, which is crucial to speech synthesis. However, the feature extraction procession tends to depend heavily on human knowledge resulting in a less expressive description of the origin audio. In this work, we proposed RawNet, a complete end-to-end neural vocoder following the auto-encoder structure for speaker-dependent and -independent speech synthesis. It automatically learns to extract features and recover audio using neural networks, which include a coder network to capture a higher representation of the input audio and an autoregressive voder network to restore the audio in a sample-by-sample manner. The coder and voder are jointly trained directly on the raw waveform without any human-designed features. The experimental results show that RawNet achieves a better speech quality using a simplified model architecture and obtains a faster speech generation speed at the inference stage.

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