Call for Papers

We invite contributions on topics of interest that are central to the workshop theme.

Submission Types

Archival papers: Research papers presenting original empirical or theoretical results.
Non-archival papers: Previously published work, work-in-progress, and extended abstracts.

Paper Lengths

Long paper: Up to 8 pages
Short paper: Up to 4 pages
Extended Abstract: Up to 2 pages

All submission types are welcome at any length tier. Only archival accepted papers will be published in the workshop proceedings. All papers will be assessed based on their relevance to the workshop themes. Archival submissions currently under review at other venues are permitted; however, to be formally included in the workshop proceedings, authors of accepted contributions must ensure their withdrawal from any other venue where the work remains under concurrent consideration.

Papers should be submitted in the ACL format following the ACL Author Guidelines . The review process will be two-way anonymized; therefore, all identifying information must be removed from submissions.

Workshop Themes

Topics of interest include, but are not limited to:

Fairness evaluation for speech models, including methods to measure disparities across accents, genders, speaker identities, socioeconomic groups, and other factors.
Bias detection and benchmarking datasets for speech systems, including the development of datasets designed to identify and evaluate bias in speech recognition, speech generation, and speech-enabled LLMs.
Ethical considerations in speech AI development, including responsible data collection, annotation practices, transparency, accountability, and governance of speech technologies.
Evaluation of Speech LLMs for fairness and inclusivity, including methods to assess bias in speech understanding, speech generation, and multimodal speech-language systems.
Cross-lingual and multilingual fairness, with a focus on challenges faced by underrepresented languages, dialects, and low-resource speech communities.
Bias mitigation strategies for speech systems, including algorithmic approaches, training strategies, and fairness-aware adaptation techniques.
Real-world impacts of biased speech technologies, including implications for accessibility, employment, digital inclusion, and human–AI interaction.
Responsible deployment and governance of speech technologies, including best practices for monitoring, auditing, and mitigating bias in real-world applications.