A theory of (almost) zero resource speech recognition

Automatic speech recognition has matured into a commercially successful technology, enabling voice-based interfaces for smartphones, smart TVs, and many other consumer devices. The overwhelming popularity, however, is still limited to languages such as English, Japanese, and German, where vast amounts of labeled training data are available. For most other languages, it is prohibitively expensive to 1) collect and transcribe the speech data required to learn good acoustic models; and 2) acquire adequate text to estimate meaningful language models. A theory of unsupervised and semi-supervised techniques for speech recognition is therefore essential. This thesis focuses on HMM-based sequence clustering and examines acoustic modeling, language modeling, and applications beyond the components of an ASR, such as anomaly detection, from the vantage point of PAC-Bayesian theory. The first part of this thesis extends standard PAC-Bayesian bounds to address the sequential nature of speech and language signals. A novel algorithm, based on sparsifying the cluster assignment probabilities with a Renyi entropy prior, is shown to provably minimize the generalization error of any probabilistic model (e.g. HMMs). The second part examines application-specific loss functions such as cluster purity and perplexity. Empirical results on a variety of tasks -- acoustic event detection, class-based language modeling, and unsupervised sequence anomaly detection -- confirm the practicality of the theory and algorithms developed in this thesis.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Bharadwaj, Sujeeth Subramanya

Contributors dc:contributor

• Hasegawa-Johnson, Mark A.
• Levinson, Stephen E.
• Liang, Feng
• Smaragdis, Paris

• Speech recognition
• Unsupervised learning
• PAC-Bayesian theory
• Language Modeling
• Acoustic Event Detection
• anomaly detection

Statement dc:rights

• Copyright 2015 Sujeeth Bharadwaj

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/78343

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/78343