Modeling phones, keywords, topics and intents in spoken languages

Spoken Language Understanding for both rich-resource languages (RRL) and low-resource languages (LRL) is an important research area for academia and the commercial world. In the conversational situations where either the language used in speech is a minority one, or the environment is noisy, barriers will emerge between the communicators. Essentially, people would like to understand the basic components of any language spoken by others who they meet in their daily lives. On the other hand, machines can also be trained to learn the process of modeling the basic language components such as phones, keywords, topics and intents during both human/machine interactions and human/human communications. Eventually, if we can develop a machine assistant for people to understand the basic meaning of any language in speech, we could make the human world much more efficient and harmonious. This thesis addresses the problem with the help of mismatched-crowdsourcing- based distant supervision, linguistic knowledge, and corpus-based transfer learning. First we analyze the usefulness of mismatched transcripts and distinctive features, and then propose phone recognition based on the optimized inference of the phone set in the low-resource language from the clustering of the mismatched transcripts. Subsequently, the keyword discovery from the phone-level results is explored. The topic information collected in the corpus is then used as the additional knowledge for topic classification and further improving phone recognition. Based on the keyword sequence, the intents of the speaker are also eventually obtained. The experimental results show that with the help of data collection design and existing knowledge, we can achieve reasonably good machine language understanding for languages whose phones, keywords, topics, and intents were not learned before. This work will lead to further investigations in the area of spoken language understanding in any language.

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

2019

Author dc:creator

• Chen, Wenda

Contributors dc:contributor

• Hasegawa-Johnson, Mark
• Li, Haizhou
• Levinson, Stephen E.
• Varshney, Lav R.

• Clustering
• Distant Supervision, Spoken language understanding
• Spoken term detection
• Speech recognition
• Low-resource languages
• Transfer learning

Statement dc:rights

• Copyright 2019 Wenda Chen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105767

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105767