WavTTS is the first raw-waveform diffusion TTS model using DiT flow matching and multi-scale mel supervision that approaches SOTA latent zero-shot performance while beating prior end-to-end models.
Base tts: Lessons from building a billion-parameter text-to-speech model on 100k hours of data
9 Pith papers cite this work. Polarity classification is still indexing.
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FlexiSLM is the first spoken language model supporting dynamic and controllable frame rates on speech input and output, outperforming fixed-rate 7B models at high quality and enabling faster inference at lower rates like 6.25 Hz.
NüshuVoice releases the first sentence-level Nüshu TTS dataset and shows that an F0-conditioned VITS model using five-level pitch notation outperforms baselines on spectral fidelity, pitch accuracy, and intelligibility.
Appropriateness of TTS varies independently across domains while naturalness scores penalize stylized speech and reward spontaneity.
SemaVoice adds SFM-guided alignment to refine continuous speech representations in autoregressive TTS, reporting 1.71% English WER on Seed-TTS and competitiveness with open-source SOTA.
X-Voice achieves zero-shot cross-lingual voice cloning across 30 languages by using IPA as a unified phonetic representation and a two-stage training process that first generates its own audio prompts then fine-tunes without text.
A framework detects speaker drift in TTS outputs by computing cosine similarities across speech segments and using LLMs for binary classification, supported by a human-validated synthetic benchmark.
Seed-TTS models produce speech matching human naturalness and speaker similarity, with added controllability via self-distillation and reinforcement learning.
CosyVoice 2 delivers human-parity naturalness and near-lossless streaming speech synthesis by combining finite-scalar quantization, a streamlined pre-trained LLM, and chunk-aware causal flow matching on large multilingual data.
citing papers explorer
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WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling
WavTTS is the first raw-waveform diffusion TTS model using DiT flow matching and multi-scale mel supervision that approaches SOTA latent zero-shot performance while beating prior end-to-end models.
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FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model
FlexiSLM is the first spoken language model supporting dynamic and controllable frame rates on speech input and output, outperforming fixed-rate 7B models at high quality and enabling faster inference at lower rates like 6.25 Hz.
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N\"ushuVoice: Reviving the Voice of Endangered N\"ushu with Pitch-Aware Text-to-Speech
NüshuVoice releases the first sentence-level Nüshu TTS dataset and shows that an F0-conditioned VITS model using five-level pitch notation outperforms baselines on spectral fidelity, pitch accuracy, and intelligibility.
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Is Natural Always Appropriate? Investigating Naturalness and Appropriateness Across Different Domains for TTS Evaluation
Appropriateness of TTS varies independently across domains while naturalness scores penalize stylized speech and reward spontaneity.
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SemaVoice: Semantic-Aware Continuous Autoregressive Speech Synthesis
SemaVoice adds SFM-guided alignment to refine continuous speech representations in autoregressive TTS, reporting 1.71% English WER on Seed-TTS and competitiveness with open-source SOTA.
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X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning
X-Voice achieves zero-shot cross-lingual voice cloning across 30 languages by using IPA as a unified phonetic representation and a two-stage training process that first generates its own audio prompts then fine-tunes without text.
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A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech
A framework detects speaker drift in TTS outputs by computing cosine similarities across speech segments and using LLMs for binary classification, supported by a human-validated synthetic benchmark.
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Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Seed-TTS models produce speech matching human naturalness and speaker similarity, with added controllability via self-distillation and reinforcement learning.
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CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models
CosyVoice 2 delivers human-parity naturalness and near-lossless streaming speech synthesis by combining finite-scalar quantization, a streamlined pre-trained LLM, and chunk-aware causal flow matching on large multilingual data.