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Self-supervised learning: A step closer to speech recognition

Published by Microsoft

The document focuses on how self-supervised learning is transforming Autonomous Speech Recognition (ASR) by eliminating the need for human-labeled data, significantly improving accuracy and scalability. This approach enables Speechmatics' models to train on over a million hours of diverse, unlabeled audio data, improving inclusivity by recognizing more dialects and voices. By addressing AI bias and enhancing speech recognition for accents and languages previously underrepresented, the technology delivers a step-change in performance. This development paves the way for broader, more inclusive applications, reshaping the future of speech recognition​.

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Related Categories Artificial Intelligence, Deep Learning, Cognitive Computing, NLP, AI Ethics, AI Integration, Sentiment Analysis, Text Mining, Speech Recognition, Machine Translation, Language Models

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