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International Conference on Text, Speech, and Dialogue

TSD 2018: Text, Speech, and Dialogue pp 342–350Cite as

Recurrent Neural Network Based Speaker Change Detection from Text Transcription Applied in Telephone Speaker Diarization System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11107))

Abstract

In this paper, we propose a speaker change detection system based on lexical information from the transcribed speech. For this purpose, we applied a recurrent neural network to decide if there is an end of an utterance at the end of a spoken word. Our motivation is to use the transcription of the conversation as an additional feature for a speaker diarization system to refine the segmentation step to achieve better accuracy of the whole diarization system. We compare the proposed speaker change detection system based on transcription (text) with our previous system based on information from spectrogram (audio) and combine these two modalities to improve the results of diarization. We cut the conversation into segments according to the detected changes and represent them by an i-vector. We conducted experiments on the English part of the CallHome corpus. The results indicate improvement in speaker change detection (by 0.5% relatively) and also in speaker diarization (by 1% relatively) when both modalities are used.

This research was supported by the Ministry of Culture Czech Republic, project No. DG16P02B009.

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Notes

  1. 1.

    Available on https://www.tensorflow.org.

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Authors and Affiliations

  1. Faculty of Applied Sciences, NTIS - New Technologies for the Information Society and Department of Cybernetics, University of West Bohemia, Univerzitní 8, 306 14, Plzeň, Czech Republic

    Zbyněk Zajíc, Daniel Soutner, Marek Hrúz, Luděk Müller & Vlasta Radová

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  1. Zbyněk Zajíc
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  2. Daniel Soutner
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  3. Marek Hrúz
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  4. Luděk Müller
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  5. Vlasta Radová
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Correspondence to Zbyněk Zajíc .

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Editors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Aleš Horák

  3. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Ivan Kopeček

  4. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Karel Pala

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Zajíc, Z., Soutner, D., Hrúz, M., Müller, L., Radová, V. (2018). Recurrent Neural Network Based Speaker Change Detection from Text Transcription Applied in Telephone Speaker Diarization System. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_37

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  • DOI: https://doi.org/10.1007/978-3-030-00794-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00793-5

  • Online ISBN: 978-3-030-00794-2

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