23 citations · 72 across the 10 of their papers we have counts for
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eess.AS2022★ 15 cited
Prosody-controllable spontaneous TTS with neural HMMs
Harm Lameris, Shivam Mehta, Gustav Eje Henter +2
Spontaneous speech has many affective and pragmatic functions that are interesting and challenging to model in TTS. However, the presence of reduced articulation, fillers, repetiti…
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…