DocumentCode
119584
Title
Automatic speech recognition system for Malay speaking children
Author
Rahman, Feisal Dani ; Mohamed, N. ; Mustafa, Mumtaz Begum ; Salim, Siti Salwah
Author_Institution
Dept. of Software Eng., Univ. of Malaya, Kuala Lumpur, Malaysia
fYear
2014
fDate
26-27 March 2014
Firstpage
79
Lastpage
82
Abstract
Automatic speech recognition or ASR system in short, is the most recent innovation in human computer interaction. An ASR system recognizes human speech and transforms them into outputs such as text or any other machine readable outputs. ASR is increasingly used in various applications such as dictation system, voice or speaker recognition and so on. Despite the advancement in the development of ASR system, not many of such system are developed for children. Children today are increasingly using computers for many daily activities including for education. The lack of ASR system for children causes them to be lagging in behind adult users. One of the reasons for the poor development of ASR system for children is the difficulties of obtaining or creating the speech corpus database of children. Unlike adults, researchers find it difficult to engage children in recording process. This research aims at developing an ASR system for Malay speaking children with the use of a small speech database. The ASR system developed in this research has the ability to recognize words at 76% accuracy.
Keywords
hidden Markov models; natural language processing; speech recognition; ASR system development; HMM; Malay speaking children; automatic speech recognition system; dictation system; hidden Markov model; human computer interaction; human speech recognition; machine readable outputs; speaker recognition; speech corpus database; voice recognition; words recognition; Frequency estimation; Hidden Markov models; ASR; Children speech corpus; HMM; Malay;
fLanguage
English
Publisher
ieee
Conference_Titel
Student Project Conference (ICT-ISPC), 2014 Third ICT International
Conference_Location
Nakhon Pathom
Print_ISBN
978-1-4799-5572-5
Type
conf
DOI
10.1109/ICT-ISPC.2014.6923222
Filename
6923222
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