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A Combined Feature Approach for Speaker Segmentation Using Convolution Neural Network

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Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10736))

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Abstract

In this paper, a speaker segmentation algorithm is proposed based on a Combined feature approach using the Convolution Neural Network (CNN), which is used to deal with the speaker segmentation problem of dialogue speech with partial prior knowledge in the CALL_CENTER environment. For the first time, the Mel-Frequency Cepstral Coefficients (MFCC) feature and the SPECTROGRAM feature are combined as the input of CNN to train the speakers’ voice feature model and to estimate the change point. In the experiments, a real database about the dialogue voice related to insurance sales and real estate sales industry is used to compare our proposed approach with Bayesian Information Criterion (BIC) approach using different acoustic features sets. The results show that the synthetical performance is improved, and our algorithm has a better segmentation.

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Acknowledgement

This work was supported in part by the National High-tech R&D Program of China (NO. 2015AA015308), Social Undertakings and Livelihood Security Science and Technology Innovation Funds of CQ CSTC (No. cstc2017shmsA20013), Frontier and Application Foundation Research Program of CQ CSTC (No. cstc2017jcyjAX0340), National Natural Science Foundation of Chi-na (No. 61402020) and Ph.D. Programs Foundation of Ministry of Education of China (No. 20130001120021).

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Authors and Affiliations

  1. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, 400030, China

    Jiang Zhong & Xue Li

  2. College of Computer Science, Chongqing University, Chongqing, 400030, China

    Jiang Zhong & Pan Zhang

  3. School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia

    Xue Li

Authors
  1. Jiang Zhong
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  2. Pan Zhang
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  3. Xue Li
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Corresponding author

Correspondence to Pan Zhang .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Zhong, J., Zhang, P., Li, X. (2018). A Combined Feature Approach for Speaker Segmentation Using Convolution Neural Network. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_54

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  • DOI: https://doi.org/10.1007/978-3-319-77383-4_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77382-7

  • Online ISBN: 978-3-319-77383-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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