Minimum generation error training with direct log spectral distortion on LSPs for HMM-based speech synthesis
By Yi-Jian Wu, Keiichi Tokuda. 2008
20 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Speech & Audio, 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
- Yoshua Bengio
- Quoc V. Le
- Oriol Vinyals
- Mohit Bansal
- Ruslan Salakhutdinov
- Kyunghyun Cho
- Sergey Levine
- Jianfeng Gao
In Speech & Audio
- Junichi Yamagishi
- Heiga Zen
- Aäron van den Oord
- Tomoki Toda
- Karen Simonyan
- Zhenhua Ling
- Xin Eric Wang
- Sander Dieleman
In Signal Processing
- Junichi Yamagishi
- Heiga Zen
- Aäron van den Oord
- Sander Dieleman
- Dong Yu
- Karen Simonyan
- Oriol Vinyals
- Yu Zhang
Citations in the corpus
- WaveNet: A Generative Model for Raw Audio (2016, background): “There have been attempts to integrate these two steps into a single one (Toda & Tokuda, 2008; Wu & Tokuda, 2008; Maia et al., 2010; Nakamura et al., 2014; Muthukumar & Black, 2014; Tokuda & Zen, 2015; 2016; Takaki & Yamagishi, 2016).”
- Directly modeling speech waveforms by neural networks for statistical parametric speech synthesis (2015, methodology): “the log spectral distortion-version of minimum generation error training (MGELSD) [11], statistical vocoder (STAVOCO) [12], waveform-level statistical model [13], and mel-cepstral analysis-integrated hidden Markov models (HMMs) [14].”
- Directly modeling voiced and unvoiced components in speech waveforms by neural networks (2016, background): “Index Terms— Statistical parametric speech synthesis; neural network; wavefom”