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...
Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged. Yet existing benchmarks typically evaluate prosodic perception, response appropriateness, and task-oriented dialogue in isolation, making it difficult to test whether prosodic evidence changes downstream decisions.
The paper evaluates bias in phoneme-based automatic speech recognition (ASR) systems, focusing on WhisperIPA and ZIPA, which produce International Phonetic Alphabet (IPA) transcriptions. Using multilingual speech corpora and demographically annotated English datasets, the authors compare model-generated IPA against grapheme-to-phoneme (G2P) outputs with both standard phoneme error rate (PER) and a new Soft PER metric that allows linguistically similar substitutions. The study finds persistent disparities across language, gender, accent, ethnicity, and age, even when accounting for acceptable phonemic variation.
By Maneesha Rani Saha, Catherine Bao, Neal Patwari
arXiv:2608. 19515v1 Announce Type: new Abstract: Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged.
By Xinyi Liu, Hooshang Nayyeri, Dilek Hakkani-Tur, Emine Yilmaz, JK Kim, Yifei Zhang, Charith Peris, Hari Thadakamalla
The paper introduces BASIS-Bench, a benchmark that separates demographic robustness, stereotypical bias, and personalization in speech large language models (LLMs). It evaluates eight recent speech LLMs, finding that while many models detect demographic cues, they rarely use them for appropriate personalization, and explicit demographic labels can worsen stereotypical associations. The study underscores the necessity of training speech LLMs to discern when demographic information should influence responses.
By Kshitij Ambilduke, Caroline Brun, Thibaut Thonet, Vassilina Nikoulina, Marcely Zanon Boito
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
The paper highlights that AI voice assistants using ASR and LLMs struggle with regional British accents because most ASR models are trained on American English. It introduces CavaBench, a benchmark of spoken financial queries, to evaluate ASR models and their impact on downstream tool‑calling accuracy across British accents. The study finds that while WER predicts tool‑calling accuracy, it may not fully capture task‑level performance, revealing accent‑related failures that vary by model and acoustic conditions.