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Hierarchical Multi-Task Transformers for Crosslingual Low Resource Phoneme Recognition

  • This paper proposes a method for multilingual phoneme recognition in unseen, low resource languages. We propose a novel hierarchical multi-task classifier built on a hybrid convolution-transformer acoustic architecture where articulatory attribute and phoneme classifiers are optimized jointly. The model was evaluated on a subset of 24 languages from the Mozilla Common Voice corpus. We found that when using regular multi-task learning, negative transfer effects occurred between attribute and phoneme classifiers. They were reduced by the hierarchical architecture. When evaluating zero-shot crosslingual transfer on a data set with 95 languages, our hierarchical multi-task classifier achieves an absolute PER improvement of 2.78% compared to a phoneme-only baseline.

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Metadaten
Author:Kevin GlockerORCiD, Munir GeorgesORCiD
Language:English
Document Type:Conference Paper
Conference:5th International Conference on Natural Language and Speech Processing (ICNLSP 2022), Trento (Italy), 16.-17.12.2022
Year of first Publication:2022
published in (English):Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022)
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg
ISBN:978-1-959429-36-4
First Page:187
Last Page:192
Review:peer-review
Open Access:ja
Tag:multi-task learning; multilingual; speech recognition; zero-shot
URN:urn:nbn:de:bvb:573-30544
URL:https://aclanthology.org/2022.icnlsp-1.21
Faculties / Institutes / Organizations:Fakultät Informatik
AImotion Bavaria
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/02/14