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Audio Classifier for Endangered Language Analysis and Education

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (AIED 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1831))

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Abstract

Around 42% of the world languages are considered endangered due to the decline in the number of speakers. MeTILDA (Melodic Transcription in Language Documentation and Application) is a collaborative platform created for researchers, teachers, and students to interact, teach, and learn endangered languages. It is currently being developed and tested on the Blackfoot language, an endangered language primarily spoken in Northwest Montana, USA and Southern Alberta, Canada. This study extends MeTILDA functionality by incorporating machine learning framework in documenting, analyzing, and educating endangered languages. Specifically, this application focuses on two main components, namely audio classifier and language learning. Here, the audio classifier component allows users to automatically obtain instances of vowels and consonants in Blackfoot audio files. The language learning component enables users to visually study the pitch patterns of these instances and improve their pronunciation by comparing with that of native speakers using a perceptual scale. This application reduces manual efforts and time-intensive tasks in locating important segments of Blackfoot language for research and educational purpose.

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Notes

  1. 1.

    fieldworks.sil.org

  2. 2.

    https://archive.mpi.nl/tla/elan

  3. 3.

    https://www.fon.hum.uva.nl/praat/

  4. 4.

    https://www.babbel.com/

  5. 5.

    https://www.rosettastone.com/

  6. 6.

    Dr. Mizuki Miyashita, a professor of linguistics at University of Montana (UM) and Mr. Naatosi Fish, a Blackfoot community linguist.

  7. 7.

    https://github.com/timmahrt/praatIO.

  8. 8.

    https://metilda.herokuapp.com/

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Acknowledgments

This work is supported by National Science Foundation (NSF BCS-2109654). We also appreciate the late Mr. Earl Old Person for his audio recording as a native speaker and the learners of the Blackfoot language.

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Authors and Affiliations

  1. University of Washington Bothell, Bothell, WA, 98011, USA

    Meghna Reddy & Min Chen

Authors
  1. Meghna Reddy
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  2. Min Chen
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Corresponding author

Correspondence to Min Chen .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. University of Leeds, Leeds, UK

    Vania Dimitrova

  4. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  5. UNED, Madrid, Spain

    Olga C. Santos

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Reddy, M., Chen, M. (2023). Audio Classifier for Endangered Language Analysis and Education. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_37

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  • DOI: https://doi.org/10.1007/978-3-031-36336-8_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36335-1

  • Online ISBN: 978-3-031-36336-8

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