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Comparison of Speech Recognition and Natural Language Understanding Frameworks for Detection of Dangers with Smart Wearables

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12745))

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Abstract

Wearable IoT devices that can register and transmit human voice can be invaluable in personal situations, such as summoning assistance in emergency healthcare situations. Such applications would benefit greatly from automated voice analysis to detect and classify voice signals. In this paper, we compare selected Speech Recognition (SR) and Natural Language Understanding (NLU) frameworks for Cloud-based detection of voice-based assistance calls. We experimentally test several services for speech-to-text transcription and intention recognition available on selected large Cloud platforms. Finally, we evaluate the influence of the manner of speaking and ambient noise on the quality of recognition of emergency calls. Our results show that many services can correctly translate voice to text and provide a correct interpretation of caller intent. Still, speech artifacts (tone, accent, diction), which can differ even for each individual in various situations, significantly influences the performance of speech recognition.

This work was supported by pro-quality grant for highly scored publications or issued patents (grant No 02/100/RGJ21/0009), the professorship grant (02/020/RGP19/0184) of the Rector of the Silesian University of Technology, Gliwice, Poland, and partially, by Statutory Research funds of Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland (grant No BK-221/RAu7/2021).

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Notes

  1. 1.

    VoxForge open speech dataset with transcribed speech: http://www.voxforge.org/home/downloads/speech/english.

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Author information

Authors and Affiliations

  1. Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Dariusz Mrozek, Szymon Kwaśnicki & Stanisław Kozielski

  2. Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Bożena Małysiak-Mrozek & Krzysztof Tokarz

  3. Department of Computer Science, Emory University, Atlanta, GA, 30322, USA

    Vaidy Sunderam

Authors
  1. Dariusz Mrozek
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  2. Szymon Kwaśnicki
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  3. Vaidy Sunderam
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  4. Bożena Małysiak-Mrozek
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  5. Krzysztof Tokarz
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  6. Stanisław Kozielski
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Corresponding author

Correspondence to Dariusz Mrozek .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Mrozek, D., Kwaśnicki, S., Sunderam, V., Małysiak-Mrozek, B., Tokarz, K., Kozielski, S. (2021). Comparison of Speech Recognition and Natural Language Understanding Frameworks for Detection of Dangers with Smart Wearables. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_36

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  • Print ISBN: 978-3-030-77969-6

  • Online ISBN: 978-3-030-77970-2

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