189 citations · 683 across the 22 of their papers we have counts for
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cs.LG2022★ 189 cited
Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models
Simon Alexanderson, Rajmund Nagy, Jonas Beskow +1
Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for synthe…
eess.AS2022★ 8 cited
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…