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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.
A third of tested configurations in depression detection collapsed to a single-class prediction, underscoring the hidden pitfalls of current aggregation methods.
