TSELM：基于离散Token与语言模型的目标说话人提取
TSELM: Target Speaker Extraction using Discrete Tokens and Language Models
AI总结：
本研究提出新型目标说话人提取网络TSELM，以WavLM离散层为输入token，结合交叉注意力、语言模型与可扩展HiFi-GAN，通过交叉熵损失将音频生成回归问题转为分类任务，在语音质量上表现优异、可懂度效果可比。
摘要：
我们提出了TSELM，这是一种利用离散token和语言模型的新型目标说话人提取网络。TSELM将来自WavLM的多个离散化层用作输入token，并结合交叉注意力机制以整合目标说话人信息。该方法采用语言模型捕捉序列依赖关系，同时使用可扩展的HiFi-GAN从token中重建音频。通过应用交叉熵损失，TSELM对输出token的概率分布建模，从而将音频生成这一复杂的回归问题转化为分类任务。实验结果表明，TSELM在语音质量上取得了优异效果，在语音可懂度上也达到了可比水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
