Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
An artificial Intelligence Approach to improving Speech Recognition
Speech Recognition is a technology with promising applications. However, the performance of current speech recognizers greatly limit their widespread use. Approaches to reducing the word error rate have mainly been associated with statistical techniques. As a consequence, speech recognition results...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2014
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613217693368320 |
|---|---|
| access_status_str | Open Access |
| author | Lopes, Luis Ramos dos Santos |
| author2 | Mashao, Daniel |
| author_browse | Lopes, Luis Ramos dos Santos Mashao, Daniel |
| author_facet | Mashao, Daniel Lopes, Luis Ramos dos Santos |
| author_sort | Lopes, Luis Ramos dos Santos |
| collection | Thesis |
| description | Speech Recognition is a technology with promising applications. However, the performance of current speech recognizers greatly limit their widespread use. Approaches to reducing the word error rate have mainly been associated with statistical techniques. As a consequence, speech recognition results can still contain sentences that are nonsensical. The method proposed here, is to analize the output of any chosen speech recognition system, in order to determine whether a sentence contains syntactic or semantic errors. This is done via a software agent that uses the information from its knowledge base to attempt to correct the errors found. A system was implemented with a small vocabulary speaker-independent continuous speech recognition system, with limited sentence structures. The achieved increase in speech recognition accuracy, shows that there are bene ts in using this approach. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/5180 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:38.580Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/5180 An artificial Intelligence Approach to improving Speech Recognition Lopes, Luis Ramos dos Santos Mashao, Daniel Ventura, Neco Engineering Speech Recognition is a technology with promising applications. However, the performance of current speech recognizers greatly limit their widespread use. Approaches to reducing the word error rate have mainly been associated with statistical techniques. As a consequence, speech recognition results can still contain sentences that are nonsensical. The method proposed here, is to analize the output of any chosen speech recognition system, in order to determine whether a sentence contains syntactic or semantic errors. This is done via a software agent that uses the information from its knowledge base to attempt to correct the errors found. A system was implemented with a small vocabulary speaker-independent continuous speech recognition system, with limited sentence structures. The achieved increase in speech recognition accuracy, shows that there are bene ts in using this approach. 2014-07-31T10:55:06Z 2014-07-31T10:55:06Z 2009 Master Thesis Masters MSc http://hdl.handle.net/11427/5180 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Engineering Lopes, Luis Ramos dos Santos An artificial Intelligence Approach to improving Speech Recognition |
| thesis_degree_str | Master's |
| title | An artificial Intelligence Approach to improving Speech Recognition |
| title_full | An artificial Intelligence Approach to improving Speech Recognition |
| title_fullStr | An artificial Intelligence Approach to improving Speech Recognition |
| title_full_unstemmed | An artificial Intelligence Approach to improving Speech Recognition |
| title_short | An artificial Intelligence Approach to improving Speech Recognition |
| title_sort | artificial intelligence approach to improving speech recognition |
| topic | Engineering |
| url | http://hdl.handle.net/11427/5180 |
| work_keys_str_mv | AT lopesluisramosdossantos anartificialintelligenceapproachtoimprovingspeechrecognition AT lopesluisramosdossantos artificialintelligenceapproachtoimprovingspeechrecognition |