arXiv:2605.30457v1 Announce Type: cross Abstract: Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels. By isolating explicit regional accent landmarks and using a phoneme-based forced aligner (ZIPA), our targeted feature set captures diale
The proliferation of powerful self-supervised learning (SSL) speech models creates an opportunity to develop more nuanced methods for sociophonetic analysis without relying on scarce or unreliable labeled data.
This research provides a pathway to more granular and accurate accent and dialect classification, which has implications for advancements in speech recognition, natural language processing, and personalized AI services.
The ability to accurately extract accent features using only acoustic labels simplifies the data annotation process and potentially improves the performance of AI models working with diverse linguistic input.
- · AI researchers (speech)
- · Language tech companies
- · Developers of personalized AI
- · Linguists
- · Companies reliant on manual sociolinguistic data labeling
Improved accuracy and efficiency in regional accent identification for Brazilian Portuguese and potentially other languages.
Development of more robust and unbiased speech AI systems that better understand and interact with diverse accents.
Enhanced personalization in voice interfaces and greater accessibility for users speaking less common or regional dialects, fostering wider AI adoption.
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