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Multimodal Spoken Unit Discovery with Paired and Unpaired Modalities
Multimodal Spoken Unit Discovery with Paired and Unpaired Modalities
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102849
- ISBN
- 9798291563816
- DDC
- 004
- 저자명
- Wang, Liming.
- 서명/저자
- Multimodal Spoken Unit Discovery with Paired and Unpaired Modalities
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 190 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: A.
- 주기사항
- Advisor: Hasegawa-Johnson, Mark.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약This 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.
- 일반주제명
- Computer science
- 일반주제명
- Electrical engineering
- 일반주제명
- Linguistics
- 일반주제명
- Acoustics
- 기타저자
- University of Illinois at Urbana-Champaign Electrical & Computer Eng
- 기본자료저록
- Dissertations Abstracts International. 87-02A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■0820 ▼a004
■1001 ▼aWang, Liming.
■24510▼aMultimodal Spoken Unit Discovery with Paired and Unpaired Modalities
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a190 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: A.
■500 ▼aAdvisor: Hasegawa-Johnson, Mark.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aThis 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.
■590 ▼aSchool code: 0090.
■650 4▼aComputer science
■650 4▼aElectrical engineering
■650 4▼aLinguistics
■650 4▼aAcoustics
■653 ▼aAcoustic unit discovery
■653 ▼aLow-resource speech recognition
■653 ▼aUnsupervised speech recognition
■653 ▼aMultimodal learning
■653 ▼aSelf-supervised learning
■653 ▼aLanguage acquisition
■690 ▼a0544
■690 ▼a0984
■690 ▼a0290
■690 ▼a0986
■71020▼aUniversity of Illinois at Urbana-Champaign▼bElectrical & Computer Eng.
■7730 ▼tDissertations Abstracts International▼g87-02A.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365892▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
