TSELM: Target Speaker Extraction using Discrete Tokens and Language Models

• URL: http://arxiv.org/abs/2409.07841v3
• Date: Tue, 17 Sep 2024 01:41:32 GMT
• Title: TSELM: Target Speaker Extraction using Discrete Tokens and Language Models
• Authors: Beilong Tang, Bang Zeng, Ming Li,
• Abstract summary: TSELM is a novel target speaker extraction network that leverages discrete tokens and language models. We show that TSELM achieves excellent results in speech quality and comparable results in speech intelligibility.
• Score: 5.187669487527287
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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