Developing a model of speech recognition process from an autopoietic approach

Speech recognition is a time-tested research into the possibilities of reproducing human like qualities in machines such as computers. In the 40 years of exploration in this field, there are many milestones that have been set. Among the recent advances in speech recognition, the deployment of statis...

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Main Authors: Liew, Eng Siang, Ahmad, Abd. Manan
Format: Conference or Workshop Item
Language:English
Published: 2000
Subjects:
Online Access:http://eprints.utm.my/10994/
http://eprints.utm.my/10994/1/LiewEngSiang2000_DevelopingaModelofSpeechRecognition.pdf
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author Liew, Eng Siang
Ahmad, Abd. Manan
author_facet Liew, Eng Siang
Ahmad, Abd. Manan
author_sort Liew, Eng Siang
building UTeM Institutional Repository
collection Online Access
description Speech recognition is a time-tested research into the possibilities of reproducing human like qualities in machines such as computers. In the 40 years of exploration in this field, there are many milestones that have been set. Among the recent advances in speech recognition, the deployment of statistical techniques like Hidden Markov Models and connectionist techniques like Time Delayed Neural Network have rendered speech recognition into a very mature and commercially viable venture. Therefore, the direction of research in this paper is aimed at the improvement of current techniques rather than reinventing the wheel; particularly, autopoiesis is introduced in this paper to give a flair of self-organization to the cognitive process of speech recognition. It is hoped that this paper would provide complimentary alternatives to speech recognition rather than exclusive solutions it.
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spelling utm-109942010-11-19T02:32:23Z http://eprints.utm.my/10994/ Developing a model of speech recognition process from an autopoietic approach Liew, Eng Siang Ahmad, Abd. Manan QA75 Electronic computers. Computer science Speech recognition is a time-tested research into the possibilities of reproducing human like qualities in machines such as computers. In the 40 years of exploration in this field, there are many milestones that have been set. Among the recent advances in speech recognition, the deployment of statistical techniques like Hidden Markov Models and connectionist techniques like Time Delayed Neural Network have rendered speech recognition into a very mature and commercially viable venture. Therefore, the direction of research in this paper is aimed at the improvement of current techniques rather than reinventing the wheel; particularly, autopoiesis is introduced in this paper to give a flair of self-organization to the cognitive process of speech recognition. It is hoped that this paper would provide complimentary alternatives to speech recognition rather than exclusive solutions it. 2000-11 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/10994/1/LiewEngSiang2000_DevelopingaModelofSpeechRecognition.pdf Liew, Eng Siang and Ahmad, Abd. Manan (2000) Developing a model of speech recognition process from an autopoietic approach. In: National Conference on Telecommunication Technology 2000, 20th - 21st Nov. 2000, Hyatt Regency Hotel, Johor Bahru.
spellingShingle QA75 Electronic computers. Computer science
Liew, Eng Siang
Ahmad, Abd. Manan
Developing a model of speech recognition process from an autopoietic approach
title Developing a model of speech recognition process from an autopoietic approach
title_full Developing a model of speech recognition process from an autopoietic approach
title_fullStr Developing a model of speech recognition process from an autopoietic approach
title_full_unstemmed Developing a model of speech recognition process from an autopoietic approach
title_short Developing a model of speech recognition process from an autopoietic approach
title_sort developing a model of speech recognition process from an autopoietic approach
topic QA75 Electronic computers. Computer science
url http://eprints.utm.my/10994/
http://eprints.utm.my/10994/1/LiewEngSiang2000_DevelopingaModelofSpeechRecognition.pdf