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arXiv · 2409.07841

TSELM: Target Speaker Extraction using Discrete Tokens and Language Models

Beilong Tang·Bang Zeng·Ming Li
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Abstract

We propose TSELM, a novel target speaker extraction network that leverages discrete tokens and language models. TSELM utilizes multiple discretized layers from WavLM as input tokens and incorporates cross-attention mechanisms to integrate target speaker information. Language models are employed to capture the sequence dependencies, while a scalable HiFi-GAN is used to reconstruct the audio from the tokens. By applying a cross-entropy loss, TSELM models the probability distribution of output tokens, thus converting the complex regression problem of audio generation into a classification task. Experimental results show that TSELM achieves excellent results in speech quality and comparable results in speech intelligibility.

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Beilong Tang, Bang Zeng, Ming Li. 2024-09-12. TSELM: Target Speaker Extraction using Discrete Tokens and Language Models. https://arxiv.org/abs/2409.07841

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