Recent development of the HMM-based singing voice synthesis system - Sinsy

By Keiichiro Oura, Ayami Mase, Tomohiko Yamada, Satoru Muto, Yoshihiko Nankaku, Keiichi Tokuda. 2010

100 citations recorded by the source corpus.

Subfields: Speech & Audio

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In Speech & Audio

1. Junichi Yamagishi
2. Lior Wolf
3. Yi Ren
4. Zhou Zhao
5. Yossi Adi
6. Tomoki Toda
7. Adam Polyak
8. Jinglin Liu

Citations in the corpus

• Jukebox: A Generative Model for Music (2020, methodology): “Similar to TTS literature, early works use concatenative methods (Bonada & Serra, 2007) that join short segments of curated singing, and statistical parametric methods (Saino et al., 2006; Oura et al., 2010) which allow modeling of timbre from training data.”
• DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (2021, methodology): “1997; Kenmochi and Ohshita 2007) or HMM-based parametric (Saino et al. 2006; Oura et al. 2010) methods, which are kind of cumbersome and lack flexibility and harmony.”
• DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (2021, methodology): “Initial works of singing voice synthesis generate the sounds using concatenated (Macon et al., 1997; Kenmochi & Ohshita, 2007) or HMM-based parametric (Saino et al., 2006; Oura et al., 2010) The voice naturalness and diversity of SVS system have been continuously improved in recent years.”
• Children's Song Dataset for Singing Voice Research (2020, background): “Sinsy is a publicly available dataset used for singing voice synthesis but it is limited to the Japanese language [5].”
• Unsupervised Cross-Domain Singing Voice Conversion (2020, methodology): “Singing Synthesis and Conversion Previous singing synthesis methods applied unit selection methods [15] or HMM based parametric methods [16–18].”
• Speech Synthesis Based on Hidden Markov Models (2013, background): “Singing voice synthesis can be regarded as synthesis in a special language in which linguistic specifications and resulting contexts are derived from musical notes and lyrics [82], [83].”