Year: 2025
Type: conference-paper
Abstract: Advancements in AI-driven speech-based applications have transformed diverse industries ranging from healthcare to customer service. However, the increasing prevalence of non-native accented speech in global interactions poses significant challenges for speech-processing systems, which are often trained on datasets dominated by native speech. This study investigates accented English speech through articulatory and acoustic analysis, identifying simpler coordination patterns and higher average pitch than native speech. Using eigenspectra and Vocal Tract Variable-based coordination features, we establish an efficient method for quantifying accent strength without relying on resource-intensive phonetic transcriptions. Our findings provide a new avenue for research on the impacts of accents on speech intelligibility and offer insights for developing inclusive, robust speech processing systems that accommodate diverse linguistic communities.
Source: Interspeech (USB)
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W4415433017
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