New GenTSE model improves speaker extraction with generative language approach

Researchers have developed GenTSE, a novel two-stage generative language model designed to enhance target speaker extraction (TSE). This model first predicts coarse semantic tokens and then refines them into fine acoustic tokens, a separation that improves accuracy and speech quality. GenTSE utilizes continuous embeddings and a Frozen-LM Conditioning training strategy to mitigate exposure bias, outperforming previous language model-based systems in experiments. AI

IMPACT Introduces a new method for improving speech processing tasks like speaker extraction.

RANK_REASON This is a research paper detailing a new model for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

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• Haoyang Li

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