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Found 5 papers, 0 papers with code
Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations
Using speaker-disentangled codes to train LLMs for text-to-speech (TTS) allows the LLM to generate the content and the style of the speech only from the text, similarly to humans, while the speaker identity is provided by the decoder of the VC model.
Controllable Emphasis with zero data for text-to-speech
We show that this technique significantly closes the gap to methods that require explicit recordings.
CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer
In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually appropriate prosody, and c) transferring prosody at fine-grained level between any pair of seen speakers.
Distribution augmentation for low-resource expressive text-to-speech
This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
This paper proposed a novel approach for the detection and reconstruction of dysarthric speech.
