The paper examines how automatic speech recognition (ASR) systems perform on Newcastle English, a regional dialect of North‑East England, using spontaneous speech from the Diachronic Electronic Corpus of Tyneside English. It analyzes over 3,000 transcription errors, classifying them by linguistic domain and social variables such as gender, age, and socioeconomic status, and highlights that phonological variation—especially vowel quality and glottalisation—drives most errors. The study finds that error rates differ across social groups, with men and speakers at age extremes experiencing higher rates, indicating that ASR errors are socially patterned rather than random.
By Dana Serditova, Kevin Tang
arXiv:2604. 01562v2 Announce Type: replace-cross Abstract: Voice cloning is often evaluated in terms of overall quality, but less is known about accent preservation and its perceptual consequences.
By Tianle Yang, Chengzhe Sun, Phil Rose, Siwei Lyu
arXiv:2602.13047v2 Announce Type: replace
Abstract: Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for...
By Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Bahman Mirheidari, Lise Sproson, Daniel Blackburn, Heidi Christensen
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
The study examined 400 utterances from 10 politicians speaking in both Luxembourgish and French to determine how much of charismatic prosody is due to speaker identity versus language. Mixed‑effects modeling revealed that speaker identity explained most of the variance, while language contributed less but still produced systematic differences: French speech had higher shimmer and phrase‑final F0, suggesting a polite, respectful tone, whereas Luxembourgish speech showed stronger mid‑frequency spectral energy, indicating a more vocally present profile. These acoustic patterns reflect the sociolinguistic roles of Luxembourgish as an informal identity language and French as a high‑prestige institutional variety.
By Nina Hosseini-Kivanani, Nafiseh Taghva, Peter Gilles, Oliver Niebuhr
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...