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Found 12 papers, 4 papers with code
Evaluating Sampling-based Filler Insertion with Spontaneous TTS
Perceptual results show little difference between compared filler insertion models including with ground-truth, which may be due to the ambiguity of what is good filler insertion and a strong neural spontaneous TTS that produces natural speech irrespective of input.
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
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)
On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis
Prior work has shown that SSL is an effective intermediate representation in two-stage text-to-speech (TTS) for both read and spontaneous speech.
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.
Automatic Evaluation of Turn-taking Cues in Conversational Speech Synthesis
Turn-taking is a fundamental aspect of human communication where speakers convey their intention to either hold, or yield, their turn through prosodic cues.
A Comparative Study of Self-Supervised Speech Representations in Read and Spontaneous TTS
Recent work has explored using self-supervised learning (SSL) speech representations such as wav2vec2. 0 as the representation medium in standard two-stage TTS, in place of conventionally used mel-spectrograms.
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
Integrated Speech and Gesture Synthesis
Text-to-speech and co-speech gesture synthesis have until now been treated as separate areas by two different research communities, and applications merely stack the two technologies using a simple system-level pipeline.
Generating coherent spontaneous speech and gesture from text
In contrast to previous approaches for joint speech-and-gesture generation, we generate full-body gestures from speech synthesis trained on recordings of spontaneous speech from the same person as the motion-capture data.
