Impaired speech recognition
Dysarthria is a speech disorder which often leads to speech that is difficult to understand or easily misinterpreted, resulting in communication challenges for affected individuals. With recent advancements in automatic speech recognition technologies, it has the potential to assist dysarthric spe...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1811372024-11-15T12:41:28Z Impaired speech recognition Lam, Michelle Su-Ann Goh Wooi Boon College of Computing and Data Science ASWBGOH@ntu.edu.sg Computer and Information Science Dysarthria is a speech disorder which often leads to speech that is difficult to understand or easily misinterpreted, resulting in communication challenges for affected individuals. With recent advancements in automatic speech recognition technologies, it has the potential to assist dysarthric speakers in their communication needs. This paper presents the development of an automatic speech recognition system that can effectively assist such individuals in their daily communication with their counterparts. The ability of two state-of-the-art models to accurately transcribe dysarthric speech will be explored. A large language model is incorporated as an automatic speech recognition correction system to further enhance the accuracy of resulting transcriptions. The effectiveness of the resulting automatic speech recognition system as a communication assistance tool will be demonstrated together with a text-to-speech synthesizer, with both components being integrated into a mobile application that aims to recreate clearly spoken words from the original dysarthric speech. While the resulting automatic speech recognition system faced challenges in generalizing across different dysarthric datasets, including a large language model into the process yielded positive outcomes as demonstrated by the qualitative user testing. Bachelor's degree 2024-11-15T12:41:28Z 2024-11-15T12:41:28Z 2024 Final Year Project (FYP) Lam, M. S. (2024). Impaired speech recognition. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181137 https://hdl.handle.net/10356/181137 en application/pdf Nanyang Technological University |
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Computer and Information Science Lam, Michelle Su-Ann Impaired speech recognition |
description |
Dysarthria is a speech disorder which often leads to speech that is difficult to understand or easily
misinterpreted, resulting in communication challenges for affected individuals. With recent advancements in automatic speech recognition technologies, it has the potential to assist dysarthric
speakers in their communication needs. This paper presents the development of an automatic speech recognition system that can effectively assist such individuals in their daily communication with their counterparts. The ability of two state-of-the-art models to accurately transcribe dysarthric speech will be explored. A large language model is incorporated as an automatic speech recognition correction system to further enhance the accuracy of resulting transcriptions. The effectiveness of the resulting automatic speech recognition system as a communication assistance tool will be demonstrated together with a text-to-speech synthesizer, with both components being integrated into a mobile application that aims to recreate clearly spoken words from the original dysarthric speech. While the resulting automatic speech recognition system faced challenges in generalizing across different dysarthric datasets, including a large language model into the process yielded positive outcomes as demonstrated by the qualitative user testing. |
author2 |
Goh Wooi Boon |
author_facet |
Goh Wooi Boon Lam, Michelle Su-Ann |
format |
Final Year Project |
author |
Lam, Michelle Su-Ann |
author_sort |
Lam, Michelle Su-Ann |
title |
Impaired speech recognition |
title_short |
Impaired speech recognition |
title_full |
Impaired speech recognition |
title_fullStr |
Impaired speech recognition |
title_full_unstemmed |
Impaired speech recognition |
title_sort |
impaired speech recognition |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/181137 |
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1816859020801081344 |