RECEIVER OPERATING CHARACTERISTICS MEASURE FOR THE RECOGNITION OF STUTTERING DYSFLUENCIES USING LINE SPECTRAL FREQUENCIES
DOI:
https://doi.org/10.31436/iiumej.v18i1.578Abstract
Stuttering is a motor-speech disorder, having common features with other motor control disorders such as dystonia, Parkinson’s disease and Tourette’s syndrome. Stuttering results from complex interactions between factors such as motor, language, emotional and genetic. This study used Line Spectral Frequency (LSF) for the feature extraction, while using three classifiers for the identification purpose, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN) and Radial Basis Function (RBF). The UCLASS (University College London Archive of Stuttered Speech) release 1 was used as database in this research. These recordings were from people of ages 12y11m to 19y5m, who were referred to clinics in London for assessment of their stuttering. The performance metrics used for interpreting the results are sensitivity, accuracy, precision and misclassification rate. Only M1 and M2 had below 100% sensitivity for RBF. The sensitivity of M1 was found to be between 40 & 60%, therefore categorized as moderate, while that of M2 falls between 60 & 80%, classed as substantial. Overall, RBF outperforms the two other classifiers, MLP and RNN for all the performance metrics considered.Downloads
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References
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[2] Al-Alaoui, Mohamad Adnan et al. “Speech Recognition Using Artificial Neural Networks and Hidden Markov Models.†IEEE Multidisciplinary Engineering Education Magazine 3.3 (2008): 77–86. Print.
[3] Awad, SS. “The Application of Digital Speech Processing to Stuttering Therapy.†IEEE Sensing, Processing, Networking, Instrumentation and Measurement Technology Conference, IMTC 97. N.p., 1997. 1361–1367. Print.
[4] Buhmann, MD. Radial Basis Functions: Theory and Implementations. Cambridge University Press, 2003. Print.
[5] Bullinaria, JA. Introduction to Neural Networks. University of Birmingham, UK, 2004. Print.
[6] Chee, Lim Sin et al. “Automatic Detection of Prolongations and Repetitions Using LPCC.†International Conference for Technical Postgraduates 2009, TECHPOS 2009. N.p., 2009. 1–4. Web.
[7] Chee, LS et al. “MFCC Based Recognition of Repetitions and Prolongations in Stuttered Speech Using K-NN and LDA.†2009 IEEE Student Conference on Research and Development and Development (SCOReD). N.p., 2009. 146–149. Print.
[8] Chee, LS, OC Ai, and S Yaacob. “Overview of Automatic Stuttering Recognition System.†Proceedings of International Conference on Man-Machine Systems. Penanag, Malaysia: N.p., 2009. Print.
[9] Hariharan, M et al. “Speech Stuttering Assessment Using Sample Entropy and Least Square Support Vector Machine.†2012 IEEE 8th International Colloquium on Signal Processing and Its Applications (CSPA). N.p., 2012. 240–245. Print.
[10] Haykin, Simon S. Neural Networks and Learning Machines. Vol. 3. Pearson Education Upper Saddle River, 2009. Print.
[11] Helliesen, GG. “Therapy for the Severe Older Adolescent and Adult Stutterer.†The Journal of Stuttering Therapy, Advocacy and Research 3 (2008): 1–70. Print.
[12] Hollingshead, K, and PA Heeman. Using a Uniform-Weight Grammar to Model Disfluencies in Stuttered Read Speech: A Pilot Study. Oregon: N.p., 2004. Print.
[13] Howell, P, S Davis, and J Bartrip. “The University College London Archive of Stuttered Speech (UCLASS).†Journal of Speech, Language, and Hearing Research 52.2 (2009): 556–569. Print.
[14] Ismail, Saliza, and A Ahmad. “Recurrent Neural Network with Backpropagation through Time Algorithm for Arabic Recognition.†Proceedings of the 18th European Simulation Multiconference (ESM). Magdeburg, Germany: N.p., 2004. 13–16. Print.
[15] Kabal, P, and RP Ramachandran. “The Computation of Line Spectral Frequencies Using Chebyshev Polynomials.†IEEE Transactions on Acoustics, Speech and Signal Processing 34.6 (1986): 1419–1426. Print.
[16] Kleijn, W. Bastiaan, Tom Bäckström, and Paavo Alku. “On Line Spectral Frequencies.†IEEE Signal Processing Letters 10.3 (2003): 75–77. Print.
[17] Kumar, Rajeev et al. “Multilingual Speaker Recognition Using Neural Network.†Proceedings of the Frontiers of Research on Speech and Music, FRSM. N.p., 2009. 1–8. Print.
[18] Manjula, G, and MS Kumar. “Stuttered Speech Recognition For Robotic Control.†International Journal of Engineering and Innovative Technology (IJEIT) 3.12 (2014): 174–177. Print.
[19] Namburu, V. “Speech Coder Using Line Spectral Frequencies of Cascaded Second Order Predictors.†VirginiaTech, 2001. Print.
[20] Pálfy, J, and J Pospichal. “Pattern Search in Dysfluent Speech.†2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). N.p., 2012. 1–6. Print.
[21] Singla, P, K Subbarao, and JL Junkins. “Direction-Dependent Learning Approach for Radial Basis Function Networks.†IEEE Transactions on Neural Networks 18.1 (2007): 203–222. Print.
[22] Watkins, KE et al. “Structural and Functional Abnormalities of the Motor System in Developmental Stuttering.†Brain 131.1 (2008): 50–59. Print.
[23] Zhang, J, B Dong, and Y Yan. “A Computer-Assist Algorithm to Detect Repetitive Stuttering Automatically.†2013 International Conference on Asian Language Processing (IALP),. N.p., 2013. 249–252. Print.
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Published
2017-05-30
How to Cite
Alang Rashid, N. K., Alim, S. A., Nik Hashim, N. N. W., & Sediono, W. (2017). RECEIVER OPERATING CHARACTERISTICS MEASURE FOR THE RECOGNITION OF STUTTERING DYSFLUENCIES USING LINE SPECTRAL FREQUENCIES. IIUM Engineering Journal, 18(1), 193–200. https://doi.org/10.31436/iiumej.v18i1.578
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Mechatronics and Automation Engineering
