arXiv:2608. 09930v1 Announce Type: cross Abstract: Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive.
By Oluwanifemi Bamgbose, Simon Rosen, Jash Shah, Lindsay Devon Brin, Hoang H Nguyen, Anke Koelzer, Rachel Hansen, Tara Bogavelli, Fanny Riols
arXiv:2608.21176v1 Announce Type: cross
Abstract: Automatic speech quality assessment aims to predict Mean Opinion Scores (MOS) consistent with human subjective perception and is essential for evalua...
By Naiyuan Li, Li Dong, Diqun Yan
arXiv:2605.28227v2 Announce Type: replace
Abstract: Speech translation models are increasingly capable of preserving speech-specific information (e.g., speaker gender, prosody, and emphasis), yet eva...
By Maike Z\"ufle, Danni Liu, Vil\'em Zouhar, Jan Niehues
arXiv:2606. 15888v1 Announce Type: cross Abstract: Non-verbal vocalizations (NVs), such as laughter, sighs, and coughs, are important acoustic cues for emotion and intent.
By Jialong Mai, Jinxin Ji, Xiaofen Xing, Wencui Liu, Xiangmin Xu
arXiv:2606. 20137v1 Announce Type: cross Abstract: Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors.
By Masaya Kawamura, Yuma Shirahata, Kentaro Mitsui, Reo Shimizu
arXiv:2609.35952v1 Announce Type: cross
Abstract: We introduce HEAR (Human-recorded Evaluation of Audio-LLM bias by Real speakers), a large-scale, ecologically valid benchmark comprising 87k real hum...
By Shen Yan, Duc Le, Irina-Elena Veliche