189 citations · 1.4k across the 55 of their papers we have counts for
7 papers · 1 filter
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…
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…
Autovocoder: Fast Waveform Generation from a Learned Speech Representation using Differentiable Digital Signal Processing
Jacob J Webber, Cassia Valentini-Botinhao, Evelyn Williams +2
Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an intermediate representation, to decompose the task into acoustic modelling and waveform generation. A mel…
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…
Predicting pairwise preferences between TTS audio stimuli using parallel ratings data and anti-symmetric twin neural networks
Cassia Valentini-Botinhao, Manuel Sam Ribeiro, Oliver Watts +2
Automatically predicting the outcome of subjective listening tests is a challenging task. Ratings may vary from person to person even if preferences are consistent across listeners…
The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation
Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko +4
This paper reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to bui…
Wavebender GAN: An architecture for phonetically meaningful speech manipulation
Gustavo Teodoro Döhler Beck, Ulme Wennberg, Zofia Malisz +1
Deep learning has revolutionised synthetic speech quality. However, it has thus far delivered little value to the speech science community. The new methods do not meet the controll…