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Enhancement systems can significantly alter ASR outcomes, but the best choice varies by task and context, challenging the notion of a one-size-fits-all solution.
Diarization-derived features can rival complex speech embeddings in predicting language proficiency, making automated assessments more accessible and efficient.
Re-ranking can make or break user engagement, and GR2 boosts performance by over 18% by harnessing the power of LLMs in ways previously unexplored.
A new multimodal dataset links brain activity, muscle activation, and articulation in speech, opening doors to understanding the causal chain of speech production.
