19 citations · 53 across the 7 of their papers we have counts for
6 papers · 1 filter
Matcha-TTS: A fast TTS architecture with conditional flow matching
Shivam Mehta, Ruibo Tu, Jonas Beskow +2
We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM). This yields an OD…
On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis
Siyang Wang, Gustav Eje Henter, Joakim Gustafson +1
Self-supervised learning (SSL) speech representations learned from large amounts of diverse, mixed-quality speech data without transcriptions are gaining ground in many speech tech…
Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesis
Shivam Mehta, Siyang Wang, Simon Alexanderson +3
With read-aloud speech synthesis achieving high naturalness scores, there is a growing research interest in synthesising spontaneous speech. However, human spontaneous face-to-face…
Automatic Evaluation of Turn-taking Cues in Conversational Speech Synthesis
Erik Ekstedt, Siyang Wang, Éva Székely +2
Turn-taking is a fundamental aspect of human communication where speakers convey their intention to either hold, or yield, their turn through prosodic cues. Using the recently prop…
OverFlow: Putting flows on top of neural transducers for better TTS
Shivam Mehta, Ambika Kirkland, Harm Lameris +3
Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech. They combine the best features of classic statistical speech syn…
Neural HMMs are all you need (for high-quality attention-free TTS)
Shivam Mehta, Éva Székely, Jonas Beskow +1
Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using HMMs. However, neural TTS is generally not probabilistic an…