CNN-Based Phoneme Classifier from Vocal Tract MRI Learns Embedding Consistent with Articulatory Topology Article Swipe
Kicky G. van Leeuwen
,
Paula Bos
,
Stefano Trebeschi
,
Maarten J. A. van Alphen
,
Luuk Voskuilen
,
Ludi E. Smeele
,
Ferdinand van der Heijden
,
R.J.J.H. van Son
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.21437/interspeech.2019-1173
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.21437/interspeech.2019-1173
Recent advances in real-time magnetic resonance imaging (rtMRI) of the vocal tract provides opportunities for studying human speech. This modality together with acquired speech may enable the mapping of articulatory configurations to acoustic features. In this study, we take the first step by training a deep learning model to classify 27 different phonemes from midsagittal MR images of the vocal tract.An American English database was used to train a convolutional neural network for classifying vowels (13 classes), consonants (14 classes) and all phonemes (27 classes) of 17 subjects. Classification top-1 accuracy of the test set for all phonemes was 57%. Erroranalysis showedvoiced and unvoiced sounds often being confused. Moreover, we performed principal component analysis on the network’s embedding and observed topological similarities between thenetwork learned representation and the vowel diagram.Saliency maps gaveinsight intothe anatomical regions most important for classification and show congruence with knownregions of articulatory importance.We demonstrate the feasibility for deep learning to distinguish between phonemes from MRI. Network analysis can be used to improve understanding of normal articulation and speech and, in the future, impaired speech. This study brings us a step closer to the articulatory-to-acoustic mapping from rtMRI.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21437/interspeech.2019-1173
- OA Status
- green
- Cited By
- 11
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2972691962
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2972691962Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21437/interspeech.2019-1173Digital Object Identifier
- Title
-
CNN-Based Phoneme Classifier from Vocal Tract MRI Learns Embedding Consistent with Articulatory TopologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-09-13Full publication date if available
- Authors
-
Kicky G. van Leeuwen, Paula Bos, Stefano Trebeschi, Maarten J. A. van Alphen, Luuk Voskuilen, Ludi E. Smeele, Ferdinand van der Heijden, R.J.J.H. van SonList of authors in order
- Landing page
-
https://doi.org/10.21437/interspeech.2019-1173Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://research.utwente.nl/en/publications/206b3e47-055c-490c-bcb8-83dfc91f2fb6Direct OA link when available
- Concepts
-
Vocal tract, Computer science, Embedding, Speech recognition, Classifier (UML), Artificial intelligence, Pattern recognition (psychology), Topology (electrical circuits), Mathematics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1, 2021: 3, 2020: 6Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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