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Staff Thesis 216 views

Automatic Speech Recognition: From Study to Practice

Sara Sharifzadeh Orcid Logo

Swansea University Author: Sara Sharifzadeh Orcid Logo

Abstract

Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this ar...

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Published: University of Autonoma de Barcelona 2010
Online Access: https://repository.lboro.ac.uk/articles/educational_resource/Automatic_speech_recognition_from_study_to_practice/9577682
URI: https://cronfa.swan.ac.uk/Record/cronfa65619
first_indexed 2024-04-07T13:40:07Z
last_indexed 2024-11-25T14:16:29Z
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recordtype SURis
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spelling 2024-04-07T14:40:10.3346051 v2 65619 2024-02-09 Automatic Speech Recognition: From Study to Practice a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2024-02-09 MACS Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work. Thesis University of Autonoma de Barcelona 1 1 2010 2010-01-01 https://repository.lboro.ac.uk/articles/educational_resource/Automatic_speech_recognition_from_study_to_practice/9577682 Thesis available at https://repository.lboro.ac.uk/articles/educational_resource/Automatic_speech_recognition_from_study_to_practice/9577682 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2024-04-07T14:40:10.3346051 2024-02-09T01:19:36.6098476 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sara Sharifzadeh 0000-0003-4621-2917 1
title Automatic Speech Recognition: From Study to Practice
spellingShingle Automatic Speech Recognition: From Study to Practice
Sara Sharifzadeh
title_short Automatic Speech Recognition: From Study to Practice
title_full Automatic Speech Recognition: From Study to Practice
title_fullStr Automatic Speech Recognition: From Study to Practice
title_full_unstemmed Automatic Speech Recognition: From Study to Practice
title_sort Automatic Speech Recognition: From Study to Practice
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Sara Sharifzadeh
format Staff Thesis
publishDate 2010
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://repository.lboro.ac.uk/articles/educational_resource/Automatic_speech_recognition_from_study_to_practice/9577682
document_store_str 0
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description Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, multimedia, medical and industrial application. Although many researches have been performed in this field in the past decades, there is still a lot of room to work. In order to start working in this area, complete knowledge of ASR systems as well as their weak points and problems is inevitable. Besides that, practical experience improves the theoretical knowledge understanding in a reliable way. Regarding to these facts, in this master thesis, we have first reviewed the principal structure of the standard HMM-based ASR systems from technical point of view. This includes, feature extraction, acoustic modeling, language modeling and decoding. Then, the most significant challenging points in ASR systems is discussed. These challenging points address different internal components characteristics or external agents which affect the ASR systems performance. Furthermore, we have implemented a Spanish language recognizer using HTK toolkit. Finally, two open research lines according to the studies of different sources in the field of ASR has been suggested for future work.
published_date 2010-01-01T08:28:07Z
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score 11.048149

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