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Overlapping Speech Detection with Cluster-based HMM Framework

Authors:

Seunghyung Lee,

Juntae Kim,

Jinuk Park,

Minsoo HahnAuthors Info & Claims

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

Pages 138 - 141

https://doi.org/10.1145/3015166.3015208

Published: 21 November 2016 Publication History

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Abstract

Overlapping speech is known to be the major source of error in various speech processing algorithm. Many previous studies on overlapping speech detection focus on exploring the various feature set for representing speech and overlapping speech characteristics while using the HMM framework. In this study, however, we hypothesize that the capacity of single HMM will not be enough to cover the whole speech and overlapping speech distribution. Thus, we proposed a simple cluster-based HMM framework to construct multiple speech and overlapping speech model. The experimental results on GRID corpus show significant improvements compare to the conventional overlap detection system.

References

[1]

Yella, S.H. and Bourlard, H. 2014. Overlapping speech detection using long-term conversational features for speaker diarization in meeting room conversations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22, 12 (Dec. 2014), 1688--1700.

Digital Library

Google Scholar

[2]

Cetin, O. and Shriberg, E. 2006. Speaker overlaps and ASR errors in meetings: Effects before, during, and after the overlap. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (Toulouse, France, 2006), 357--360.

Google Scholar

[3]

Tsai, W. and Liao, S. 2010. Speaker identification in overlapping speech, Journal of Information Science and Engineering, 26, 1891--1903.

Google Scholar

[4]

Miro, X. A., Bozonnet, S., Evans, N., Fredouille, C., Friedland, G. and Vinyals, O. 2012. Speaker diarization: A review of recent research. IEEE Transactions on Audio, Speech, and Language Processing, 20, 2 (Feb. 2012), 356--370.

Digital Library

Google Scholar

[5]

Yella, S.H. and Bourlard, H. 2013. Improved overlap speech diarization of meeting recordings using long-term conversational features. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(Vancouver, Canada, 2013), 7746--7750.

Google Scholar

[6]

Wrigley, S.N., Brown, G.J., Wan, V. and Renals, S. 2005. Speech and crosstalk detection in multi-channel audio. IEEE Transactions on Speech and Audio Processing, 13, 1 (Jan. 2005), 84--91.

Crossref

Google Scholar

[7]

Zelenak, M., Segura, C., Luque, J. and Hernando, J. 2012. Simultaneous speech detection with spatial features for speaker diarization. IEEE Transactions on Audio, Speech, and Language Processing, 20, 2 (Feb 2012), 436--446.

Digital Library

Google Scholar

[8]

Boakye, K., Vinyals, O. and Friedland, G. 2011. Improved overlapped speech handling for speaker diarization. In Proceedings of the INTERSPEECH (Florence, Italy, 2011), 941--944.

Google Scholar

[9]

Vipperla, R., Geiger, J.T., Bozonnet, S., Wang, D., Evans, N., Schuller, B. and Rigoll, G. 2012. Speech overlap detection and attribution using convolutive non-negative sparse coding. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (Toulouse, France, 2012), 4181--4184.

Google Scholar

[10]

Geiger, J.T., Eyben, F., Schuller, B.and Rigoll, G. 2013. Detecting overlapping speech with long-term short memory recurrent neural networks. In Proceedings of the INTERSPEECH (Lyon, France, 2013).

Google Scholar

[11]

Cooke, M.P., Barker, J., Cunningham, S. and Shao, X. 2006. An audio-visual corpus for speech perception and automatic speech recognition. The Journal of the Acoustical Society of America, 120, 5 (Nov. 2006), 2421--2424.

Crossref

Google Scholar

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Nakamura KGomez R(2017)Improving separation of overlapped speech for meeting conversations using uncalibrated microphone array2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)10.1109/ASRU.2017.8268916(55-62)Online publication date: Dec-2017
https://doi.org/10.1109/ASRU.2017.8268916

Overlapping Speech Detection with Cluster-based HMM Framework
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Hardware
  1. Communication hardware, interfaces and storage

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ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

November 2016

235 pages

ISBN:9781450347907

DOI:10.1145/3015166

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Published: 21 November 2016

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ICSPS 2016: 8th International Conference on Signal Processing Systems

November 21 - 24, 2016

Auckland, New Zealand

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ICSPS 2016 Paper Acceptance Rate 46 of 83 submissions, 55%;

Overall Acceptance Rate 46 of 83 submissions, 55%

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Nakamura KGomez R(2017)Improving separation of overlapped speech for meeting conversations using uncalibrated microphone array2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)10.1109/ASRU.2017.8268916(55-62)Online publication date: Dec-2017
https://doi.org/10.1109/ASRU.2017.8268916

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Detection of Overlapping Speech for the Purposes of Speaker Diarization

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Detection of Overlapping Speech for the Purposes of Speaker Diarization

Discriminative feature weighting for HMM-based continuous speech recognizers

Modeling and interpolation of Austrian German and Viennese dialect in HMM-based speech synthesis

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