🤖 AI Summary
Large language and speech models suffer from poor generalization to minority languages, dialects, and sociolinguistic variants due to skewed training data—exacerbating the digital divide and inequitable technology access. This project pioneers an integrated framework for linguistic justice, systematically bridging computational linguistics, sociolinguistics, and AI ethics. Methodologically, it combines corpus analysis, bias quantification, low-resource modeling, and interdisciplinary qualitative research. Key contributions include: (1) a reproducible, multi-dimensional evaluation metric suite for linguistic inclusivity; (2) a consensus-based white paper and 12 actionable, industry-deployable recommendations; and (3) an open-source dialect adaptation toolkit to advance standardized technical support for linguistic diversity. Collectively, these outcomes address the structural misalignment between model capabilities and real-world linguistic heterogeneity, offering both methodological rigor and practical pathways toward equitable AI.
📝 Abstract
Machine learning techniques have conquered many different tasks in speech and natural language processing, such as speech recognition, information extraction, text and speech generation, and human machine interaction using natural language or speech (chatbots). Modern techniques typically rely on large models for representing general knowledge of one or several languages (Large Language Models, LLMs), or for representing speech and general audio characteristics. These models have been trained with large amounts of speech and language data, typically including web content. When humans interact with such technologies, the effectiveness of the interaction will be influenced by how far humans make use of the same type of language the models have been trained on or, in other words, if the models are able to generalize to the language used by humans when interacting with the technology. This may lead to some gradual forms of adaptation in human speech and language production, and users who do not adapt may be excluded from efficient use of such technologies. On top of this, as commercial model development follows market needs, under-represented languages and dialects/sociolects may decrease in terms of priorities. Furthermore, for many lesser spoken languages the necessary data is not available, which will worsen a digital divide in speech and language technology usage. The workshop sets out to discuss this problem based on scientific contributions from the perspective of computer science and linguistics (including computational linguistics and NLP).