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Found 5 papers, 3 papers with code
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech
With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach for zero-shot Text-to-Speech (TTS) synthesis.
Mini-Batch Optimization of Contrastive Loss
Contrastive learning has gained significant attention as a method for self-supervised learning.
RedPen: Region- and Reason-Annotated Dataset of Unnatural Speech
We present a novel speech dataset, RedPen, with human annotations on unnatural speech regions and their corresponding reasons.
DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
The majority of current Text-to-Speech (TTS) datasets, which are collections of individual utterances, contain few conversational aspects.
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech
Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.
