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Found 9 papers, 2 papers with code
Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation
The multi-codebook speech codec enables the application of large language models (LLM) in TTS but bottlenecks efficiency and robustness due to multi-sequence prediction.
WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark
Furthermore, we have created subsets of varying sizes, categorized by segment quality scores to allow for TTS model training and fine-tuning.
Text-aware and Context-aware Expressive Audiobook Speech Synthesis
Recent advances in text-to-speech have significantly improved the expressiveness of synthetic speech.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
Transfer the linguistic representations from TTS to accent conversion with non-parallel data
This paper introduces a novel non-autoregressive framework for accent conversion that learns accent-agnostic linguistic representations and employs them to convert the accent in the source speech.
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion
Specifically, to flexibly adapt to the dynamic-variant speaker characteristic in the temporal and channel axis of the speech, we propose a novel fine-grained speaker modeling method, called temporal-channel retrieval (TCR), to find out when and where speaker information appears in speech.
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
We further fuse the linguistic and para-linguistic features through an attention mechanism, where speaker-dependent prosody features are adopted as the attention query, which result from a prosody encoder with target speaker embedding and normalized pitch and energy of source speech as input.
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
To alleviate the difficulty in training, we propose to model linguistic and prosodic information by considering cross-sentence, embedded structure in training.
Building a mixed-lingual neural TTS system with only monolingual data
When deploying a Chinese neural text-to-speech (TTS) synthesis system, one of the challenges is to synthesize Chinese utterances with English phrases or words embedded.
