摘要翻译：
演讲者日记（即确定谁在何时发言？）对于多说话人的自然主义交互，如同伴领导的团队学习(PLTL)会话是一项具有挑战性的任务。在这项研究中，我们提出了一种基于混合多元von Mises-Fisher分布的鲁棒说话人聚类。我们的日记化管道有两个阶段：(i)地面-真相分割；(ii)提议的发言者群集。地面真值语音活动信息用于从每个语音片段中提取I-向量。我们用主成分分析对I-向量进行后处理，进行降维，然后进行长度归一化。归一化I-向量是具有分辨方向特征的高维单位向量。我们用一个由多元von Mises-Fisher分布组成的混合模型对归一化I-向量进行建模。选择余弦距离的K均值聚类作为基线方法。评价数据来源于:㈠CRSS-PLTL语料库；(ii)AMI语料库的三会子集。CRSSPLTL数据包含PLTL会议的音频记录，这是以学生为主导的STEM教育范式。该方法在PLTL语料库和AMI语料库中的相对改进率分别为44.48%和53.68%。索引术语：说话人聚类，冯·米塞斯-费雪分布，同伴领导的团队学习，i-Vector，自然主义音频。
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英文标题：
《Robust Speaker Clustering using Mixtures of von Mises-Fisher
  Distributions for Naturalistic Audio Streams》
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作者：
Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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英文摘要：
  Speaker Diarization (i.e. determining who spoke and when?) for multi-speaker naturalistic interactions such as Peer-Led Team Learning (PLTL) sessions is a challenging task. In this study, we propose robust speaker clustering based on mixture of multivariate von Mises-Fisher distributions. Our diarization pipeline has two stages: (i) ground-truth segmentation; (ii) proposed speaker clustering. The ground-truth speech activity information is used for extracting i-Vectors from each speechsegment. We post-process the i-Vectors with principal component analysis for dimension reduction followed by lengthnormalization. Normalized i-Vectors are high-dimensional unit vectors possessing discriminative directional characteristics. We model the normalized i-Vectors with a mixture model consisting of multivariate von Mises-Fisher distributions. K-means clustering with cosine distance is chosen as baseline approach. The evaluation data is derived from: (i) CRSS-PLTL corpus; and (ii) three-meetings subset of AMI corpus. The CRSSPLTL data contain audio recordings of PLTL sessions which is student-led STEM education paradigm. Proposed approach is consistently better than baseline leading to upto 44.48% and 53.68% relative improvements for PLTL and AMI corpus, respectively. Index Terms: Speaker clustering, von Mises-Fisher distribution, Peer-led team learning, i-Vector, Naturalistic Audio.
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PDF链接：
https://arxiv.org/pdf/1808.06045