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
Nonverbaltts: A public english corpus of text-aligned nonverbal vocalizations with emotion annotations for text-to-speech
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NVBench provides a standardized bilingual benchmark and evaluation protocol for assessing non-verbal vocalization generation, placement, and salience in text-to-speech systems.
A MoE-enhanced model with conditional distillation reduces speech-NVV EER from 38.93% to 22.66% and speech EER from 13.17% to 9.24% across 10 NVV types.
A data pipeline, 14-dimension benchmark, and decoupled fine-tuning model are presented to advance fine-grained multi-dimensional speech understanding in LLMs.
MoVE uses specialized LoRA expert adapters and a soft router to translate non-verbal vocalizations in S2ST, reproducing them in 76% of cases versus at most 14% for baselines while scoring highest on naturalness and emotional fidelity.
A tag-based annotation scheme for non-verbal vocalizations in TTS data yields higher expressiveness (eMOS 4.20) and emotion recognition accuracy (78.8%) with minor naturalness trade-offs.
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NVBench: A Benchmark for Speech Synthesis with Non-Verbal Vocalizations
NVBench provides a standardized bilingual benchmark and evaluation protocol for assessing non-verbal vocalization generation, placement, and salience in text-to-speech systems.