All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis
Thilo von Neumann,Keisuke Kinoshita,Marc Delcroix,Shoko Araki,Tomohiro Nakatani,Reinhold Haeb-Umbach +5 more
- pp 91-95
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TLDR
In this paper, an all-neural approach to simultaneous speaker counting, diarization and source separation is presented, where the neural network is recurrent over time as well as over the number of sources.Abstract:
Automatic meeting analysis comprises the tasks of speaker counting, speaker diarization, and the separation of overlapped speech, followed by automatic speech recognition. This all has to be carried out on arbitrarily long sessions and, ideally, in an online or block-online manner. While significant progress has been made on individual tasks, this paper presents for the first time an all-neural approach to simultaneous speaker counting, diarization and source separation. The NN-based estimator operates in a block-online fashion and tracks speakers even if they remain silent for a number of time blocks, thus learning a stable output order for the separated sources. The neural network is recurrent over time as well as over the number of sources. The simulation experiments show that state of the art separation performance is achieved, while at the same time delivering good diarization and source counting results. It even generalizes well to an unseen large number of blocks.read more
Citations
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Proceedings ArticleDOI
Continuous Speech Separation: Dataset and Analysis
Zhuo Chen,Takuya Yoshioka,Liang Lu,Tianyan Zhou,Zhong Meng,Yi Luo,Jian Wu,Xiong Xiao,Jinyu Li +8 more
TL;DR: A new real recording dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate conversations and capturing the audio replays with far-field microphones, which helps researchers from developing systems that can be readily applied to real scenarios.
Posted Content
Speaker Recognition Based on Deep Learning: An Overview
Zhongxin Bai,Xiao-Lei Zhang +1 more
TL;DR: Several major subtasks of speaker recognition are reviewed, including speaker verification, identification, diarization, and robust speaker recognition, with a focus on deep-learning-based methods.
Proceedings ArticleDOI
End-to-end speaker diarization for an unknown number of speakers with encoder-decoder based attractors
TL;DR: In this article, an encoder-decoder based attractor calculation (EDA) method was proposed to generate a flexible number of attractors from a speech embedding sequence to produce the same number of speaker activities.
Posted Content
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors
TL;DR: This paper proposes a method for encoder-decoder based attractor calculation (EDA), which first generates a flexible number of attractors from a speech embedding sequence, and then the generated multiple attractors are multiplied by the speechembedding sequence to produce the same number of speaker activities.
Journal ArticleDOI
Wavesplit: End-to-End Speech Separation by Speaker Clustering
Neil Zeghidour,David Grangier +1 more
TL;DR: Wavesplit as mentioned in this paper infers a set of source representations via clustering, which addresses the fundamental permutation problem of separation, and then estimates each source signal given the inferred representations.
References
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Proceedings ArticleDOI
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Book ChapterDOI
The AMI meeting corpus: a pre-announcement
Jean Carletta,Simone Ashby,Sebastien Bourban,Michael J. Flynn,Maël Guillemot,Thomas Hain,Jaroslav Kadlec,Vasilis Karaiskos,Wessel Kraaij,Melissa Kronenthal,Guillaume Lathoud,Mike Lincoln,Agnes Lisowska,Iain McCowan,Wilfried Post,Dennis Reidsma,Pierre Wellner +16 more
TL;DR: The AMI Meeting Corpus as mentioned in this paper is a multi-modal data set consisting of 100 hours of meeting recordings, which is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly.