Unsupervised methods for evaluating speech representations
Name
1192545151-MIT.pdf
Size
2.73 MB
Format
Adobe PDF
Checksum (MD5)
7cdfb299238f7b85d096c6b7cbc380a5
Author(s)
Gump, Michael H.
Advisor(s)
James R. Glass.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Unsupervised representation learning using deep generative models has produced remarkable results across many domains in recent years. These methods have been applied to speech processing to learn representations useful for downstream supervised tasks like speaker, dialect, or phoneme identification. One research path has been to develop general purpose priors that select effective representations. However, many priors on good representations are difficult to incorporate into unsupervised methods because they are difficult to evaluate without supervision. This thesis proposes to use low-level acoustic features to address this problem for speech. By using techniques in acoustic processing, we develop methods for structured evaluation for speech representations. The evaluation aims both to assess the efficacy of representations for downstream tasks and to validate claims about the priors used to construct them. An evaluation suite for benchmarking and analyzing research in speech representation learning is produced and open-sourced as a result of this thesis.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 47-50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
Persistent DSpace Link
