2026-09-19T02:43:01Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1180592021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

James R. Glass. Hsu, Wei-Ning, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-09-17T15:55:42Z 2018-09-17T15:55:42Z 2018 2018 http://hdl.handle.net/1721.1/118059 1051460462 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 121-128). Despite recent successes in machine learning, artificial intelligence is still far from matching human intelligence in many ways. Two important aspects are transferability and amount of supervision required. Take speech recognition for example: while humans can easily adapt to a new accent without explicit supervision (i.e., ground truth transcripts for speech of a new accent), current machine learning techniques still struggle with such a scenario. We argue that an essential component of human learning is unsupervised or weakly supervised representation learning, which transforms input signals to low dimensional representations that facilitate subsequent structured learning and knowledge acquisition. In this thesis, we develop unsupervised representation learning frameworks for speech data. We start with investigating an existing variational autoencoder (VAE) model for learning latent representations, and derive novel latent space operations for speech transformation. The transformation method is applied to unsupervised domain adaptation problems, which addresses the transferability issues of supervised machine learning framework. We then extend the VAE models, and propose a novel factorized hierarchical variational autoencoder (FHVAE), which better models a generative process of sequential data, and learns not only disentangled, but also interpretable latent representations without any supervision. By leveraging the interpretability, we demonstrate that such representations can be applied to a wide range of tasks, including but not limited to: voice conversion, denoising, speaker verification, speaker invariant phonetic feature extraction, and noise invariant phonetic feature extraction. In the last part of this thesis, we examine scalability issues regarding the original FHVAE training algorithm in terms of runtime, memory, and optimization stability. Based on our analysis, we propose a hierarchical sampling algorithm for training, which enables training of FHVAE models on arbitrarily large datasets. by Wei-Ning Hsu. S.M. 128 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Unsupervised learning of disentangled representations for speech with neural variational inference models Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a9d9b9a3-c824-4224-9fd9-999e6a387dea"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Unsupervised learning of disentangled representations for speech with neural variational inference models</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Hsu, Wei-Ning, Ph. D. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Despite recent successes in machine learning, artificial intelligence is still far from matching human intelligence in many ways. Two important aspects are transferability and amount of supervision required. Take speech recognition for example: while humans can easily adapt to a new accent without explicit supervision (i.e., ground truth transcripts for speech of a new accent), current machine learning techniques still struggle with such a scenario. We argue that an essential component of human learning is unsupervised or weakly supervised representation learning, which transforms input signals to low dimensional representations that facilitate subsequent structured learning and knowledge acquisition. In this thesis, we develop unsupervised representation learning frameworks for speech data. We start with investigating an existing variational autoencoder (VAE) model for learning latent representations, and derive novel latent space operations for speech transformation. The transformation method is applied to unsupervised domain adaptation problems, which addresses the transferability issues of supervised machine learning framework. We then extend the VAE models, and propose a novel factorized hierarchical variational autoencoder (FHVAE), which better models a generative process of sequential data, and learns not only disentangled, but also interpretable latent representations without any supervision. By leveraging the interpretability, we demonstrate that such representations can be applied to a wide range of tasks, including but not limited to: voice conversion, denoising, speaker verification, speaker invariant phonetic feature extraction, and noise invariant phonetic feature extraction. In the last part of this thesis, we examine scalability issues regarding the original FHVAE training algorithm in terms of runtime, memory, and optimization stability. Based on our analysis, we propose a hierarchical sampling algorithm for training, which enables training of FHVAE models on arbitrarily large datasets.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>