BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES

The end-to-end (E2E) model is influentially reshaping the automatic speech recognition (ASR) scene, supplanting traditional ASR models such as the Hidden Markov model (HMM) and Deep Neural Network (DNN)-based hybrid models. In essence, it displaces crucial components of these traditional ASR models...

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Main Author: GERALD EINSTEIN CORNELIUS
Format: Final Year Project Report
Language:English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
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Online Access:http://ir.unimas.my/id/eprint/44201/1/Gerald%20Einstein%20ft.pdf
http://ir.unimas.my/id/eprint/44201/
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spelling my.unimas.ir.442012024-01-18T03:34:45Z http://ir.unimas.my/id/eprint/44201/ BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES GERALD EINSTEIN CORNELIUS PE English The end-to-end (E2E) model is influentially reshaping the automatic speech recognition (ASR) scene, supplanting traditional ASR models such as the Hidden Markov model (HMM) and Deep Neural Network (DNN)-based hybrid models. In essence, it displaces crucial components of these traditional ASR models by simplifying the module-based design into a single-network architecture inside a deep learning framework. Interestingly, this simplified technique does not hinder the performance of this worthy successor of a model in recognising speech, while it even yields results that are superior to those of traditional ASR models. Recognising its infinite potential, OpenAI have developed the robust Whisper model based on the E2E, encoder-decoder transformer. While the aforementioned model performs exceptionally well for English ASR, its undetermined performance on low resource languages is a topic of research interest. In this work, the performance evaluation of the Whisper model on Sarawak languages will be explored. This model will be evaluated using speech data from under-resourced Sarawak languages, namely the Sarawak Malay, Iban, Melanau, and the Bidayuh dialects of Jagoi and Bukar Sadong. Fundamentally, a systematic literature review (SLR) and the development of an ASR system built on the Whisper model to uncover the recognition accuracy of Whisper OpenAI on Sarawak languages are the key highlights of this work. The experiment results obtained from the developed ASR system, based on the Word Error Rate (WER) evaluation metric may serve as a baseline for future works based on the integrated Whisper model for under-resource Sarawak languages. Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44201/1/Gerald%20Einstein%20ft.pdf GERALD EINSTEIN CORNELIUS (2023) BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic PE English
spellingShingle PE English
GERALD EINSTEIN CORNELIUS
BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES
description The end-to-end (E2E) model is influentially reshaping the automatic speech recognition (ASR) scene, supplanting traditional ASR models such as the Hidden Markov model (HMM) and Deep Neural Network (DNN)-based hybrid models. In essence, it displaces crucial components of these traditional ASR models by simplifying the module-based design into a single-network architecture inside a deep learning framework. Interestingly, this simplified technique does not hinder the performance of this worthy successor of a model in recognising speech, while it even yields results that are superior to those of traditional ASR models. Recognising its infinite potential, OpenAI have developed the robust Whisper model based on the E2E, encoder-decoder transformer. While the aforementioned model performs exceptionally well for English ASR, its undetermined performance on low resource languages is a topic of research interest. In this work, the performance evaluation of the Whisper model on Sarawak languages will be explored. This model will be evaluated using speech data from under-resourced Sarawak languages, namely the Sarawak Malay, Iban, Melanau, and the Bidayuh dialects of Jagoi and Bukar Sadong. Fundamentally, a systematic literature review (SLR) and the development of an ASR system built on the Whisper model to uncover the recognition accuracy of Whisper OpenAI on Sarawak languages are the key highlights of this work. The experiment results obtained from the developed ASR system, based on the Word Error Rate (WER) evaluation metric may serve as a baseline for future works based on the integrated Whisper model for under-resource Sarawak languages.
format Final Year Project Report
author GERALD EINSTEIN CORNELIUS
author_facet GERALD EINSTEIN CORNELIUS
author_sort GERALD EINSTEIN CORNELIUS
title BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES
title_short BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES
title_full BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES
title_fullStr BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES
title_full_unstemmed BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES
title_sort benchmarking whisper openai on sarawak languages
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44201/1/Gerald%20Einstein%20ft.pdf
http://ir.unimas.my/id/eprint/44201/
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score 13.209306