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Human-Centered AI for Disordered Speech Recognition - Katarzyna Foremniak
Oct 10, 2024
Katarzyna Foremniak, a seasoned computational linguist with over a decade of experience in NLP and speech recognition, shares her insights. She discusses the complexities of automatic speech recognition, particularly for disordered speech, and the challenges of articulation variability. With anecdotes about consonant clusters and amusing voice recognition mishaps in automotive systems, Kasia emphasizes the need for human-centered AI and personalized ASR models to enhance communication for diverse speech patterns.
48:01
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Quick takeaways
- Human-centered AI aims to enhance speech recognition by addressing diverse user speech patterns, including those with disorders and accents.
- The interdisciplinary nature of computational linguistics plays a crucial role in developing effective algorithms for modeling and understanding human language.
Deep dives
Human-Centered AI and Speech Recognition
The discussion centers around the concept of human-centered AI, particularly in the context of speech recognition technology. Human-centered AI focuses on tailoring AI solutions to better serve human users and their diverse speech patterns. As speech recognition systems typically rely on standard speech patterns, it is important to address the need for models that can effectively recognize and understand atypical productions, whether due to speech disorders or regional accents. Enhancements in technology, such as large language models (LLMs), are providing new opportunities to improve recognition accuracy in these varied contexts.
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