Directly modeling speech waveforms by neural networks for statistical parametric speech synthesis

By Keiichi Tokuda, Heiga Zen. 2015

50 citations recorded by the source corpus.

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

1. Frank K. Soong
2. Heiga Zen
3. Lei He
4. Junichi Yamagishi
5. Yao Qian
6. Xu Tan
7. Keiichi Tokuda
8. Yuchen Fan

In Signal Processing

1. Frank K. Soong
2. Lei He
3. Heiga Zen
4. Yao Qian
5. Keiichi Tokuda
6. Junichi Yamagishi
7. Yuchen Fan
8. Michiel Bacchiani

Citations in the corpus

• Directly modeling voiced and unvoiced components in speech waveforms by neural networks (2016, background): “This paper extends the previous approach [11] to have both the voiced and unvoiced components in its speech signal model.”
• SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping (2022, background): “Some time-domain filters can be considered for the covariance matrix [38,39].”
• Speaker and language factorization in DNN-based TTS synthesis (2016, background): “Deep Neural Networks (DNNs) have advanced parametric Text-to-Speech (TTS) synthesis to a new frontier [2, 3, 4, 5, 6, 7, 8, 9, 10].”