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This research investigates whether speech representations that excel at identifying regional accents in read speech maintain that capability when transferring to spontaneous speech, revealing a significant performance drop for models like Whisper. The study identifies style-invariance as the key factor for robustness, demonstrating that high accuracy under matched conditions does not guarantee reliable accent detection across different speaking styles. These findings are critical for developers building voice interfaces that must handle diverse user inputs without losing demographic sensitivity.
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Do Speech Representations Preserve Regional Accent Across Read and Spontaneous Speech?

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.06430v1 Announce Type: new Abstract: Regional accent cues can be captured under matched conditions, but it remains unclear whether they persist between read and spontaneous speech. We study RVG1, with 500 German speakers from nine regions, comparing ten speech representations on regional classification and continuous geolocation under matched conditions and speaker-independent read--spontaneous transfer. Whisper performs best under matched conditions, reaching 0.489 nine-way UAR and 148 km median geolocation error, but drops to 0.11/0.18 UAR across transfer directions and 363 km geolocation error. Self-supervised models show a similar degradation, whereas speaker embeddings are less discriminative in-domain but more robust under transfer. This contrast is consistent across classification and geolocation. Across representations, robustness is associated with how little a representation shifts between styles (style-invariance), for which crossstyle speaker retrieval is an interpretable proxy. Age, sex, sentence-overlap, and duration controls do not account for the gap, although channel characteristics contribute. These results show that strong matched-condition performance does not indicate robust regional information.

Abstract:Regional accent cues can be captured under matched conditions, but it remains unclear whether they persist between read and spontaneous speech. We study RVG1, with 500 German speakers from nine regions, comparing ten speech representations on regional classification and continuous geolocation under matched conditions and speaker-independent read--spontaneous transfer. Whisper performs best under matched conditions, reaching 0.489 nine-way UAR and 148 km median geolocation error, but drops to 0.11/0.18 UAR across transfer directions and 363 km geolocation error. Self-supervised models show a similar degradation, whereas speaker embeddings are less discriminative in-domain but more robust under transfer. This contrast is consistent across classification and geolocation. Across representations, robustness is associated with how little a representation shifts between styles (style-invariance), for which crossstyle speaker retrieval is an interpretable proxy. Age, sex, sentence-overlap, and duration controls do not account for the gap, although channel characteristics contribute. These results show that strong matched-condition performance does not indicate robust regional information.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Computation and Language (cs.CL); Sound (cs.SD)

arXiv:2610.06430 [cs.CL]

(or arXiv:2610.06430v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.06430 arXiv-issued DOI via DataCite (pending registration)

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