Adding more training data improves voice-type classification more than architecture or representation changes, which plateau near 50% average F-score, still below human annotator agreement of about 70%.
They enable effortless, long-duration recordings, offering unprecedented insights into children’s language environments [1]
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Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
Adding more training data improves voice-type classification more than architecture or representation changes, which plateau near 50% average F-score, still below human annotator agreement of about 70%.