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Recent advances in neural network -based text-to-speech have reached humanlevel naturalness in synthetic speech. The present sequence-to-sequence modelscan directly map text to mel-spectrogram acoustic features, which areconvenient for modeling, but present additional challenges for vocoding (i.e.,waveform generation from the acoustic features). High-quality synthesis can beachieved with neural vocoders, such as WaveNet, but such autoregressive modelssuffer from slow sequential inference. Meanwhile, their existing parallelinference counterparts are difficult to train and require increasingly largemodel sizes. In this paper, we propose an alternative training strategy for aparallel neural vocoder utilizing generative adversarial networks, andintegrate a linear predictive synthesis filter into the model. Results showthat the proposed model achieves significant improvement in inference speed,while outperforming a WaveNet in copy-synthesis quality.

GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram