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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V oxcpm: Tokenizer-free tts for context-aware speech generation and true-to-life voice cloning
22 Pith papers cite this work. Polarity classification is still indexing.
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SpeechEditBench provides seven atomic editing tasks, compositional multi-operation instructions, and an anchor-based protocol yielding target success, preservation success, and joint success metrics; evaluations show no model excels across dimensions and compositional editing is especially difficult
MINT-Bench is a new benchmark using hierarchical taxonomy, multi-stage data pipeline, and hybrid evaluation to assess instruction-following TTS systems, revealing major gaps in compositional and paralinguistic controls.
A Taiwan-specific tokenizer, language model, and bridge to a reused acoustic stack cut code-switching TTS CER from 11.45% to 4.81%, with 65.6% listener preference.
By training flow-matching TTS to avoid augmented repeat/skip latent trajectories, RobustSpeechFlow cuts Seed-TTS-eval WER from 1.44 to 1.38 and improves CER on a new multilingual benchmark.
S2ST-Omni 2 uses typology-informed hierarchical encoding, gated Dual-CTC, and typology-aware prompting to improve multilingual S2ST over flat-label baselines on CVSS-C, with gains in low-data regimes.
RTCFake is the first large-scale dataset of real-time communication speech deepfakes paired with offline versions, paired with a phoneme-guided consistency learning method that improves cross-platform and noise-robust detection.
TTS-PRISM defines a 12-dimensional perceptual schema, builds a targeted diagnostic dataset via adversarial synthesis and expert labels, and tunes an end-to-end model that outperforms generalist LLMs in human alignment on a 1,600-sample Mandarin test set while profiling six TTS paradigms.
OmniVoice introduces a diffusion language model-style non-autoregressive TTS system that directly maps text to multi-codebook acoustic tokens, scaling zero-shot synthesis to over 600 languages with SOTA results on multilingual benchmarks using 581k hours of open data.
Qwen3-TTS delivers state-of-the-art multilingual TTS performance with 3-second voice cloning, description control, and ultra-low-latency streaming via dual tokenizers and a dual-track LM architecture trained on over 5 million hours of data.
AMR dynamically routes audio (W2V-BERT 2.0) and face (IResNet-18) embeddings via adapters and a KL-supervised router, reaching 99.07% average accuracy on POLY-SIM 2026 protocols and beating the FOP baseline by 32.73%.
VoxCPM2 scales hierarchical continuous-latent speech modeling to 2B parameters and over 2M hours of multilingual data, unifying voice cloning, style control, and continuation in one backbone with open release.
Raon-OpenTTS provides an open 510K-hour curated speech dataset and DiT-based TTS models up to 1B parameters that achieve competitive WER and speaker similarity on benchmarks versus closed models trained on millions of hours.
JaiTTS-v1.0 achieves 1.94% CER on short Thai speech, beating human ground truth of 1.98%, matches humans on long speech, and wins 283 of 400 human comparisons against commercial systems.
A shared LoRA adapter on VoxCPM2 trained on 26 hours of Khmer and Korean data improves Khmer MOS from 3.85 to 4.23 using under 3% of parameters, with no gain for Korean.
SARA is a dual-stream VAE that integrates semantic and acoustic streams to achieve high-fidelity reconstruction and natural zero-shot TTS without complex regularizers.
MELD jointly optimizes a discrete latent variable encoder on mel-spectrograms with an autoregressive speech LM, claiming gains over codec and mel baselines on zero-shot TTS/STT plus fewer autoregressive artifacts.
PilotTTS achieves lowest WER 1.50% (en) and CER 0.87% (zh) plus highest speaker similarity on Seed-TTS Eval using a Q-Former conditioned autoregressive architecture and a released multi-stage open data pipeline.
Joint-marginal distillation with adaptive weighting is reported as the best overall alignment loss for speech VAEs across reconstruction, understanding, and generation.
Authors submit a cross-lingual voice cloning system to IWSLT 2026 using OmniVoice fine-tuned on ensemble-distilled synthetic data, reporting gains in WER, CER, and speaker similarity for scientific texts in three languages.
The paper announces the ISCSLP 2026 CoT-TTS Challenge with text- and audio-context tracks, large-scale bilingual datasets, and a Qwen3-based baseline requiring both reasoning output and speech generation.
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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SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing
SpeechEditBench provides seven atomic editing tasks, compositional multi-operation instructions, and an anchor-based protocol yielding target success, preservation success, and joint success metrics; evaluations show no model excels across dimensions and compositional editing is especially difficult
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MINT-Bench: A Comprehensive Multilingual Benchmark for Instruction-Following Text-to-Speech
MINT-Bench is a new benchmark using hierarchical taxonomy, multi-stage data pipeline, and hybrid evaluation to assess instruction-following TTS systems, revealing major gaps in compositional and paralinguistic controls.
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BlueMagpie-TTS: A Token-Efficient Tokenizer, Language Model, and TTS for Taiwanese-Accent Code-Switching Speech
A Taiwan-specific tokenizer, language model, and bridge to a reused acoustic stack cut code-switching TTS CER from 11.45% to 4.81%, with 65.6% listener preference.
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RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching
By training flow-matching TTS to avoid augmented repeat/skip latent trajectories, RobustSpeechFlow cuts Seed-TTS-eval WER from 1.44 to 1.38 and improves CER on a new multilingual benchmark.
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From Flat Language Labels to Typological Priors: Structured Language Conditioning for Multilingual Speech-to-Speech Translation
S2ST-Omni 2 uses typology-informed hierarchical encoding, gated Dual-CTC, and typology-aware prompting to improve multilingual S2ST over flat-label baselines on CVSS-C, with gains in low-data regimes.
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RTCFake: Speech Deepfake Detection in Real-Time Communication
RTCFake is the first large-scale dataset of real-time communication speech deepfakes paired with offline versions, paired with a phoneme-guided consistency learning method that improves cross-platform and noise-robust detection.
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TTS-PRISM: A Perceptual Reasoning and Interpretable Speech Model for Fine-Grained Diagnosis
TTS-PRISM defines a 12-dimensional perceptual schema, builds a targeted diagnostic dataset via adversarial synthesis and expert labels, and tunes an end-to-end model that outperforms generalist LLMs in human alignment on a 1,600-sample Mandarin test set while profiling six TTS paradigms.
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OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
OmniVoice introduces a diffusion language model-style non-autoregressive TTS system that directly maps text to multi-codebook acoustic tokens, scaling zero-shot synthesis to over 600 languages with SOTA results on multilingual benchmarks using 581k hours of open data.
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Qwen3-TTS Technical Report
Qwen3-TTS delivers state-of-the-art multilingual TTS performance with 3-second voice cloning, description control, and ultra-low-latency streaming via dual tokenizers and a dual-track LM architecture trained on over 5 million hours of data.
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AMR: Adaptive Modality Routing for Multimodal Polyglot Speaker Identification
AMR dynamically routes audio (W2V-BERT 2.0) and face (IResNet-18) embeddings via adapters and a KL-supervised router, reaching 99.07% average accuracy on POLY-SIM 2026 protocols and beating the FOP baseline by 32.73%.
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VoxCPM2 Technical Report
VoxCPM2 scales hierarchical continuous-latent speech modeling to 2B parameters and over 2M hours of multilingual data, unifying voice cloning, style control, and continuation in one backbone with open release.
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Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech
Raon-OpenTTS provides an open 510K-hour curated speech dataset and DiT-based TTS models up to 1B parameters that achieve competitive WER and speaker similarity on benchmarks versus closed models trained on millions of hours.
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JaiTTS: A Thai Voice Cloning Model
JaiTTS-v1.0 achieves 1.94% CER on short Thai speech, beating human ground truth of 1.98%, matches humans on long speech, and wins 283 of 400 human comparisons against commercial systems.
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Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean
A shared LoRA adapter on VoxCPM2 trained on 26 hours of Khmer and Korean data improves Khmer MOS from 3.85 to 4.23 using under 3% of parameters, with no gain for Korean.
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SARA: A Dual-Stream VAE for High-Fidelity Speech Generation via Integrating Semantic and Acoustic Representations
SARA is a dual-stream VAE that integrates semantic and acoustic streams to achieve high-fidelity reconstruction and natural zero-shot TTS without complex regularizers.
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MELD: Mel-Spectrogram-Based Speech Language Modeling with Discrete Latent Variables
MELD jointly optimizes a discrete latent variable encoder on mel-spectrograms with an autoregressive speech LM, claiming gains over codec and mel baselines on zero-shot TTS/STT plus fewer autoregressive artifacts.
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PilotTTS: A Disciplined Modular Recipe for Competitive Speech Synthesis
PilotTTS achieves lowest WER 1.50% (en) and CER 0.87% (zh) plus highest speaker similarity on Seed-TTS Eval using a Q-Former conditioned autoregressive architecture and a released multi-stage open data pipeline.
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On the Distillation Loss Functions of Speech VAE for Unified Reconstruction, Understanding, and Generation
Joint-marginal distillation with adaptive weighting is reported as the best overall alignment loss for speech VAEs across reconstruction, understanding, and generation.
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One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech
Authors submit a cross-lingual voice cloning system to IWSLT 2026 using OmniVoice fine-tuned on ensemble-distilled synthetic data, reporting gains in WER, CER, and speaker similarity for scientific texts in three languages.
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ISCSLP 2026 CoT-TTS Challenge: Chain-of-Thought Reasoning for Context-Aware Text-to-Speech
The paper announces the ISCSLP 2026 CoT-TTS Challenge with text- and audio-context tracks, large-scale bilingual datasets, and a Qwen3-based baseline requiring both reasoning output and speech generation.
- dots.tts Technical Report