LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis
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We propose a linear prediction (LP)-based waveform generation method via WaveNet speech synthesis. The WaveNet vocoder, which uses speech parameters as a conditional input of WaveNet, has significantly improved the quality of statistical parametric speech synthesis system. However, it is still challenging to effectively train the neural vocoder when the target database becomes larger and more expressive. As a solution, the approaches that only generate the vocal source signal by the neural vocoder have been proposed. However, they tend to generate synthetic noise because the vocal source is independently handled without considering the entire speech synthesis process; where it is inevitable to come up with a mismatch between vocal source and vocal tract filter. To address this problem, we propose an LP-WaveNet that structurally models the vocal source in the speech training and inference processes. The experimental results verify that the proposed system outperforms the conventional WaveNet vocoders both objectively and subjectively.
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