基于矢量量化的可选码本半监督在线说话人二分化

Mahmoud El-Hindi, Michael Muma, A. Zoubir
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引用次数: 0

摘要

说话人分类系统通过根据说话人的身份标记语音片段来处理音频文件。许多扬声器拨号系统离线工作,不适合在线应用。我们提出了一个半监督、在线、低复杂度的系统。虽然一般来说,说话人分界是以一种无监督的方式进行的,但所呈现的系统依赖于对话中参与说话人的登记。该系统有两个主要的新颖之处。第一个是提出的在线学习策略,该策略根据处理后的片段对学习说话人的有用性进行评估,即用它更新说话人模型。使用两个度量来评估段,以确定是否使用该段更新系统。第二个新颖的方面是提出的矢量量化方法,该方法不仅根据目标说话人的码本建模得分,而且还考虑了另一个码本。我们还提出了一种计算替代码本的方法。仿真结果表明,所提出的系统优于没有提出在线学习策略的同类系统,并显示出优势,特别是在短训练时间下。
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Semi-Supervised Online Speaker Diarization using Vector Quantization with Alternative Codebooks
Speaker diarization systems process audio files by labelling speech segments according to speakers' identities. Many speaker diarization systems work offline and are not suited for online applications. We present a semi-supervised, online, low-complexity system. While, in general, speaker diarization operates in an unsupervised manner, the presented system relies on the enrollment of the participating speakers in the conversation. The diarization system has two main novel aspects. The first one is a proposed online learning strategy that evaluates processed segments according to their usefulness for learning a speaker, i.e. update a speaker model with it. The segment is evaluated using two metrics to determine whether to use the segment to update the system. The second novel aspect is a proposed vector quantization approach that models the score not only depending on the target speaker codebook but also takes an alternative codebook into account. We also present an approach to compute the alternative codebook. Simulation results show that the proposed system outperforms a comparable system without the proposed online learning strategy and shows benefits, especially for short training lengths.
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