Automatic Diagnosis of Distortion Type of Arabic /r/ Phoneme Using Feed Forward Neural Network

The paper is not for recognizing normal formed speech but for distorted speech via examining the ability of feed forward Artificial Neural Networks (ANN) to recognize speech flaws. In this paper we take the Arabic /r/ phoneme distortion that is somewhat common among native speakers as a case study.To do this, r-Distype program is developed as a script written using Praat speech processing software tool. r-Distype program automatically develops a feed forward ANN that tests the spoken word (which includes /r/ phoneme) to detect any possible type of distortion. Multiple feed forward ANNs of different architectures have been developed and their achievements reported. Training data and testing data of the developed ANNs are sets of spoken Arabic words that contain /r/ phoneme in different positions so they cover all distortion types of Arabic /r/ phoneme. These sets of words were produced by different genders and different ages.The results obtained from developed ANNs were used to draw a conclusion about automating the detection of pronunciation problems in general.Such computerised system would be a good tool for diagnosing speech flaws and gives a great help in speech therapy. Also, the idea itself may open a new research subarea of speech recognition that is automatic speech therapy. Keywords: Distortion, Arabic /r/ phoneme, articulation disorders, Artificial Neural Network, Praat

[1]  Javier Macías Guarasa,et al.  Novel Applications of Neural Networks in Speech Technology Systems: Search Space Reduction and Prosodic Modeling , 2009, Intell. Autom. Soft Comput..

[2]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[3]  LiMin Fu,et al.  Neural networks in computer intelligence , 1994 .

[4]  Nikos Fakotakis,et al.  Comparison of Speech Features on the Speech Recognition Task , 2007 .

[5]  K. Versteegh The Arabic Language , 1997 .

[6]  Hua Nong Ting,et al.  Speech analysis and classification using neural networks for computer-based malay speech therapy , 2001 .

[7]  Chin Luh Tan,et al.  Digit Recognition Using Neural Networks , 2004 .

[8]  Ibtisam H. Jameel,et al.  The Articulation Disorders in Producing the Arabic /r/ Descriptive and Analytical Study , 2010 .

[9]  Ayad Tareq Imam,et al.  Relative-Fuzzy: A Novel Approach for Handling Complex Ambiguity for Software Engineering of Data Mining Models , 2010 .

[10]  J. Tebelskis,et al.  Speech Recognition Using Neural Networks , 1996 .

[11]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[12]  N. H. Mahmood,et al.  Applications of cascade-forward neural networks for nasal, lateral and trill arabic phonemes , 2012, 2012 8th International Conference on Information Science and Digital Content Technology (ICIDT2012).

[13]  Zengjun Xiang,et al.  A neural network model for Chinese speech synthesis , 1990, IEEE International Symposium on Circuits and Systems.

[14]  Nivja H. Jong,et al.  Praat script to detect syllable nuclei and measure speech rate automatically , 2009, Behavior research methods.