标题： 基于循环谐波特征的会议中多麦克风自动语音分割 显示英文标题

标题： Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features

摘要： 说话人分割是回答音频流中谁在何时说话的任务。 管道系统依赖语音分割来提取说话人的片段并实现稳健的说话人分割。 本文提出了一种通用框架，以解决远场语音场景中的三个分割任务： 语音活动检测（VAD）、重叠语音检测（OSD）和说话人变化检测（SCD）。 在文献中，有一些研究探讨了多麦克风远场语音场景。 在此工作中，我们提出了一组基于圆谐域（CH-DOA）中到达方向估计的新空间特征。 这些空间特征从多麦克风音频数据中提取，并与标准声学特征相结合。 在AMI会议语料库上的实验表明，CH-DOA可以在麦克风关闭的情况下保持稳健性并改善分割。 显示英文摘要

摘要： Speaker diarization is the task of answering Who spoke and when? in an audio stream. Pipeline systems rely on speech segmentation to extract speakers' segments and achieve robust speaker diarization. This paper proposes a common framework to solve three segmentation tasks in the distant speech scenario: Voice Activity Detection (VAD), Overlapped Speech Detection (OSD), and Speaker Change Detection (SCD). In the literature, a few studies investigate the multi-microphone distant speech scenario. In this work, we propose a new set of spatial features based on direction-of-arrival estimations in the circular harmonic domain (CH-DOA). These spatial features are extracted from multi-microphone audio data and combined with standard acoustic features. Experiments on the AMI meeting corpus show that CH-DOA can improve the segmentation while being robust in the case of deactivated microphones.