The paper introduces a method for measuring accent differences that balances interpretability and practicality. It proposes using articulatory representations obtained via articulatory inversion as an interpretable basis for accent comparison, while employing optimal transport to compare accents across any type of recording. This approach aims to overcome the limitations of traditional phonetic analyses and embedding‑based methods, which are either time‑consuming or non‑interpretable.
By Charles McGhee, Mark J. F. Gales, Kate M. Knill
arXiv:2610.09486v1 Announce Type: cross
Abstract: Spoken language identification (LID) aims to recognize the target language regardless of accent. In practice, however, LID models fine-tuned from sel...
By Minu Kim, Jihwan Lee, David R. Mortensen, Shrikanth Narayanan
arXiv:2609.27382v1 Announce Type: cross
Abstract: Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents...
By Kian Shamsaie, Iman Modarressi
arXiv:2609.28060v1 Announce Type: new
Abstract: Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for...
By Tina Raissi, Nhan Phan, Mikko Kurimo
arXiv:2609.18533v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representation...
By Nicolas Bourrel, Abderrahmane Issam, Gerasimos Spanakis
arXiv:2608. 01281v1 Announce Type: cross Abstract: Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subword modeling.
By Saierdaer Yusuyin, Nanling Jiang, Hao Huang, Zhijian Ou
Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents and older speakers. We introduce TRIAD, an audit g...
Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representations. We ask whether speaker-linked attributes that...
Spoken language identification (LID) aims to recognize the target language regardless of accent. In practice, however, LID models fine-tuned from self-supervised speech representations frequently conf...
The paper investigates a failure mode called phonological interference in phoneme‑level multilingual speech models, where models incorrectly impose a single language’s phonology on mixed or unseen language input. Experiments show significant loss of language‑specific phonemes in code‑switched and unseen‑language scenarios, and the authors trace this effect to a low‑dimensional subspace of internal activations. They propose a windowed language estimation (WLE) repair that reduces interference by 34–69% while preserving monolingual performance.
By Moran Yanuka, Raja Giryes, Moris Alper
arXiv:2608. 06300v1 Announce Type: new Abstract: Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age.
By Arya Labroo, Mengjie Qian, Kate Knill
The paper investigates whether audio language models encode phonetic features similarly when processing spoken versus written input. By comparing mean representations of minimal phoneme pairs across six models, seven features, and 15 languages, the study finds that only voicing in two Qwen2.5-Omni models shows a significant shared direction, and that the model family—not size—determines feature representation. The analysis uses cosine similarity against a random-pair reference to assess alignment across modalities.