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oai:www.ideals.illinois.edu:2142/121497 2023-12-13 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Hasegawa-Johnson, Mark Hasegawa-Johnson, Mark Smaragdis, Paris Schwing, Alexander Fleck, Margaret 2023-08 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms The student, Liming Wang, accepted the attached license on 2023-07-10 at 18:16. The student, Liming Wang, submitted this Dissertation for approval on 2023-07-10 at 19:58. This Dissertation was approved for publication on 2023-07-12 at 10:20. DSpace SAF Submission Ingestion Package generated from Vireo submission #19612 on 2023-12-04 at 17:01:43 Multimodal spoken unit discovery with paired and unpaired modalities Wang, Liming 2023-07-12 Acoustic Unit Discovery Low-resource Speech Recognition Unsupervised Speech Recognition Multimodal Learning Self-supervised Learning Language Acquisition This thesis addresses the challenge of low-resource speech recognition by formulating it as a multimodal learning problem. The goal is to build a multimodal spoken unit discovery system that does not require any textual transcripts. Instead, it leverages speech and semantically related, multimodal signals such as paired images, unpaired text and unpaired sign language videos. To this end, this thesis proposes several novel algorithms based on neural networks and probabilistic graphical models. Further, it provides theoretical insights and empirical evidence to validate the efficacy of multimodal signals for spoken unit discovery. Text eng https://hdl.handle.net/2142/121497 Copyright 2023 by Liming Wang Ph.D. Dissertation Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng