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:2606. 19951v1 Announce Type: cross Abstract: Mean opinion score (MOS) prediction models are widely used as proxy metrics in text-to-speech (TTS) research, yet their ability to capture quality differences beyond acoustic fidelity remains unclear.
By Masato Takagi, Masaya Kawamura, Reo Shimizu, Yuma Shirahata
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:2607. 14846v1 Announce Type: cross Abstract: Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation.
By David Ayllon, Alice Baird, Jeffrey Brooks, Franc Camps-Febrer, Jakub Piotr C{\l}apa, Theo Lebryk, Jens Madsen, Olya Ossipova, Sharath Rao, Hoon Shin, Tigran Soghbatyan, Georg Streich, Rashish Tandon, Panagiotis Tzirakis
arXiv:2605. 03297v2 Announce Type: replace-cross Abstract: ASR systems based on self-supervised acoustic pretraining and CTC fine-tuning achieve strong performance on native speech but remain sensitive to accent variability.
By Van-Phat Thai, Aradhya Dhruv, Duc-Thinh Pham, Sameer Alam
The paper introduces a native-reference phone‑class geometry that measures second‑language pronunciation deviation without needing pronunciation labels, read‑aloud prompts, or matched native recordings. By averaging self‑supervised representations for each phone‑class in a native speech corpus and applying singular value decomposition, the authors create a compact coordinate system. Projecting L2 utterances into this space, they find that distances to native coordinates correlate negatively with holistic speaking proficiency and pronunciation quality, indicating the geometry captures relevant acoustic‑phonetic information for spontaneous L2 speech.
By Tina Raissi, Nhan Phan, Chenxiao Wang, Mikko Kurimo