Multimodal spoken unit discovery with paired and unpaired modalities

Wang, Liming

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https://hdl.handle.net/2142/121497 Copy

Description

Title

Multimodal spoken unit discovery with paired and unpaired modalities

Author(s)

Wang, Liming

Issue Date

2023-07-12

Director of Research (if dissertation) or Advisor (if thesis)

Hasegawa-Johnson, Mark

Doctoral Committee Chair(s)

Hasegawa-Johnson, Mark

Committee Member(s)

• Smaragdis, Paris
• Schwing, Alexander
• Fleck, Margaret

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• acoustic unit discovery
• low-resource speech recognition
• unsupervised speech recognition
• multimodal learning
• self-supervised learning
• language acquisition

Abstract

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.

Graduation Semester

2023-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/121497

Copyright and License Information

Copyright 2023 by Liming Wang

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