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.
Fish audio s2 technical report
9 Pith papers cite this work. Polarity classification is still indexing.
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2026 9representative citing papers
CapTalk unifies single-utterance and dialogue voice design via utterance- and speaker-level captions plus a hierarchical variational module for stable timbre with adaptive expression.
PRIME-Speech adds low-latency speech output to frozen S2T LLMs by synchronizing a causal post-decoder with intermediate hidden states and using mixed conditioning plus turn-level KV-cache packing, preserving original S2T performance across translation, QA, and dialogue tasks.
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.
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.
RADAR Challenge 2026 organizes a multilingual audio deepfake detection benchmark with media transformations, reporting participation from 33 development and 22 evaluation teams using EER metric.
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.
KIT's IWSLT 2026 submission adapts a multilingual TTS model with language prompting, RL fine-tuning, and reference-conditioned lexical matching, reporting largest gains from prompting.
citing papers explorer
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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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CapTalk: Unified Voice Design for Single-Utterance and Dialogue Speech Generation
CapTalk unifies single-utterance and dialogue voice design via utterance- and speaker-level captions plus a hierarchical variational module for stable timbre with adaptive expression.
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Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation
PRIME-Speech adds low-latency speech output to frozen S2T LLMs by synchronizing a causal post-decoder with intermediate hidden states and using mixed conditioning plus turn-level KV-cache packing, preserving original S2T performance across translation, QA, and dialogue tasks.
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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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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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RADAR Challenge 2026: Robust Audio Deepfake Recognition under Media Transformations
RADAR Challenge 2026 organizes a multilingual audio deepfake detection benchmark with media transformations, reporting participation from 33 development and 22 evaluation teams using EER metric.
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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.
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KIT's Submission to Cross-Lingual Voice Cloning in IWSLT 2026
KIT's IWSLT 2026 submission adapts a multilingual TTS model with language prompting, RL fine-tuning, and reference-conditioned lexical matching, reporting largest gains from prompting.
- dots.tts Technical Report