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        <title>Representation Learning for Discovering Phonemic Tone Contours</title>
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        <namePart type="given">Bai</namePart>
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            <title>Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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    <abstract>Tone is a prosodic feature used to distinguish words in many languages, some of which are endangered and scarcely documented. In this work, we use unsupervised representation learning to identify probable clusters of syllables that share the same phonemic tone. Our method extracts the pitch for each syllable, then trains a convolutional autoencoder to learn a low-dimensional representation for each contour. We then apply the mean shift algorithm to cluster tones in high-density regions of the latent space. Furthermore, by feeding the centers of each cluster into the decoder, we produce a prototypical contour that represents each cluster. We apply this method to spoken multi-syllable words in Mandarin Chinese and Cantonese and evaluate how closely our clusters match the ground truth tone categories. Finally, we discuss some difficulties with our approach, including contextual tone variation and allophony effects.</abstract>
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    <identifier type="doi">10.18653/v1/2020.sigmorphon-1.26</identifier>
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%0 Conference Proceedings
%T Representation Learning for Discovering Phonemic Tone Contours
%A Li, Bai
%A Xie, Jing Yi
%A Rudzicz, Frank
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-representation
%X Tone is a prosodic feature used to distinguish words in many languages, some of which are endangered and scarcely documented. In this work, we use unsupervised representation learning to identify probable clusters of syllables that share the same phonemic tone. Our method extracts the pitch for each syllable, then trains a convolutional autoencoder to learn a low-dimensional representation for each contour. We then apply the mean shift algorithm to cluster tones in high-density regions of the latent space. Furthermore, by feeding the centers of each cluster into the decoder, we produce a prototypical contour that represents each cluster. We apply this method to spoken multi-syllable words in Mandarin Chinese and Cantonese and evaluate how closely our clusters match the ground truth tone categories. Finally, we discuss some difficulties with our approach, including contextual tone variation and allophony effects.
%R 10.18653/v1/2020.sigmorphon-1.26
%U https://aclanthology.org/2020.sigmorphon-1.26
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.26
%P 217-223

Markdown (Informal)

[Representation Learning for Discovering Phonemic Tone Contours](https://aclanthology.org/2020.sigmorphon-1.26) (Li et al., SIGMORPHON 2020)

• Representation Learning for Discovering Phonemic Tone Contours (Li et al., SIGMORPHON 2020)

ACL

• Bai Li, Jing Yi Xie, and Frank Rudzicz. 2020. Representation Learning for Discovering Phonemic Tone Contours. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 217–223, Online. Association for Computational Linguistics.