GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
By Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi, Paavo Alku. 2019
26 citations recorded by the source corpus.
Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to mel-spectrogram acoustic features, which are convenient for modeling, but present additional challenges for vocoding (i.e., waveform generation from the acoustic features). High-quality synthesis can be achieved with neural vocoders, such as WaveNet, but such autoregressive models suffer from slow sequential inference. Meanwhile, their existing parallel inference counterparts are difficult to train and require increasingly large model sizes. In this paper, we propose an alternative training strategy for a parallel neural vocoder utilizing generative adversarial networks, and integrate a linear predictive synthesis filter into the model. Results show that the proposed model achieves significant improvement in inference speed, while outperforming a WaveNet in copy-synthesis quality.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Signal Processing
Citations in the corpus
- Parallel Wavegan: A Fast Waveform Generation Model Based on Generative Adversarial Networks with Multi-Resolution Spectrogram (2019, background): “Juvela et al [18] has proposed a similar approach (e.”
- Gaussian Lpcnet for Multisample Speech Synthesis (2020, unknown): “However, in many recent studies very good results have been achieved by combining deep learning approach with classical Source-Filter model of speech production (e.g. [5, 12, 13, 14]).”