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English(EN) SLM-SS: Token-Based Speech Separation with Speech Language Models

新的SLM-SS框架使用语音语言模型进行高级语音分离

研究人员开发了SLM-SS,一个利用语音语言模型处理多说话人混合语音的新型语音分离框架。这种基于令牌的方法将语音离散化为序列,并采用自回归模型进行说话人排序和语言内容处理,然后使用非自回归模块进行优化。SLM-SS系统在词错误率和主观质量得分方面均有显著提高,甚至在未重新训练的情况下,在处理未见的嘈杂和中文混合语音方面也优于现有方法。 AI

影响 这项研究可能为转录和语音助手等下游应用带来更清晰的语音分离系统。

排序理由 详细介绍一种新的语音分离方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SLM-SS框架使用语音语言模型进行高级语音分离

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详细介绍一种新的语音分离方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chenda Li, Tianhua Li, Yiming Chen, Wei Wang, Jiahe Wang, Xin Zhou, Yanmin Qian ·

    SLM-SS:基于令牌的语音分离与语音语言模型

    arXiv:2610.27785v1 Announce Type: cross Abstract: Speech separation (SS) has witnessed substantial progress in recent years with the advent of neural network-based methods, achieving notable improvements on signal-level evaluation metrics. Existing speech separation systems are m…