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Found 8 papers, 3 papers with code
Speech Data Augmentation for Improving Phoneme Transcriptions of Aphasic Speech Using Wav2Vec 2.0 for the PSST Challenge
As part of the PSST challenge, we explore how data augmentations, data sources, and model size affect phoneme transcription accuracy on speech produced by individuals with aphasia.
Unified speech and gesture synthesis using flow matching
As text-to-speech technologies achieve remarkable naturalness in read-aloud tasks, there is growing interest in multimodal synthesis of verbal and non-verbal communicative behaviour, such as spontaneous speech and associated body gestures.
Ranked #1 on
Motion Synthesis
on Trinity Speech-Gesture Dataset
Diffusion-Based Co-Speech Gesture Generation Using Joint Text and Audio Representation
The output of the CSMP module is used as a conditioning signal in the diffusion-based gesture synthesis model in order to achieve semantically-aware co-speech gesture generation.
Matcha-TTS: A fast TTS architecture with conditional flow matching
We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM).
Ranked #1 on
Text-To-Speech Synthesis
on LJSpeech
(MOS metric)
Diff-TTSG: Denoising probabilistic integrated speech and gesture synthesis
With read-aloud speech synthesis achieving high naturalness scores, there is a growing research interest in synthesising spontaneous speech.
Prosody-controllable spontaneous TTS with neural HMMs
Spontaneous speech has many affective and pragmatic functions that are interesting and challenging to model in TTS.
OverFlow: Putting flows on top of neural transducers for better TTS
Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech.
Ranked #11 on
Text-To-Speech Synthesis
on LJSpeech
(using extra training data)
Neural HMMs are all you need (for high-quality attention-free TTS)
Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using HMMs.
Ranked #3 on
Speech Synthesis
on LJSpeech
