Recognition of lexical tone production of children with an artificial neural network

Conclusion. This study demonstrated that the artificial neural network can successfully classify Mandarin Chinese tone patterns produced by multiple children. The neural network can be used as an objective way of evaluating tone production of children. Objectives. Traditionally, tone production is e...

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Bibliographic Details
Published in:Acta oto-laryngologica 2007-01, Vol.127 (4), p.365-369
Main Authors: Xu, Li, Chen, Xiuwu, Zhou, Ning, Li, Yongxin, Zhao, Xiaoyan, Han, Demin
Format: Article
Language:English
Subjects:
Adult
Biological and medical sciences
Child
Child, Preschool
China
Chinese tone patterns
Female
Humans
Language
Language Development
Male
Medical sciences
Neural Networks (Computer)
Otorhinolaryngology. Stomatology
pattern recognition
Phonetics
Pitch Perception
Reference Values
Sound Spectrography
Speech Production Measurement
Tone language
tone recognition
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Summary:Conclusion. This study demonstrated that the artificial neural network can successfully classify Mandarin Chinese tone patterns produced by multiple children. The neural network can be used as an objective way of evaluating tone production of children. Objectives. Traditionally, tone production is evaluated subjectively using human listeners. The aim of the present study was to investigate the efficacy of using an artificial neural network in evaluating tone production of Mandarin-speaking children. Subjects and methods. Speech materials were recorded from 61 normal-hearing children. The fundamental frequency (F0) of each monosyllabic word was extracted and then used as inputs to a feed-forward backpropagation artificial neural network. The number of inputs was set at 12, whereas the number of hidden neurons was set at 16 in the neural network. The output layer consisted of four neurons representing the four Mandarin tone patterns. The tone recognition performance of the neural network was further compared with that of native Mandarin-speaking adult listeners. Results. The neural network successfully classified the tone patterns of the 61 child speakers with an accuracy of about 85% correct. This high accuracy exceeded the tone recognition performance by the adult listeners. Individual child speakers showed varied tone production accuracy as recognized by the adult listeners or by the neural network.
ISSN:0001-6489
1651-2251
DOI:10.1080/00016480601011477