Abstract
Specific Language Impairment is a communication disorder regarding the mastery of language and conversation that impacts children. The system proposed aims to provide an alternative diagnosis method that does not rely on specific assessment tools. The system will accept a voice sample from the child and then detect indicators that differentiate individuals with specific language impairment from that voice sample. These indicators were based on the timbre and pitch characteristics of sound. Three different feature spaces are calculated, followed by derived features, with three different classifiers to determine the most accurate combination. The three feature spaces are Chroma, Mel-frequency cepstral coefficients (MFCC), and Tonnetz and the three classifiers are Support Vector Machines, Random Forest and a Recurrent Neural Network. MFCC, representing the timbre characteristic, was found to be the most accurate feature vector across all classifiers and Random Forest being the most accurate classifier across all feature spaces. The most accurate combination found was the MFCC feature vector with the Random Forest classifier with an accuracy level of 99%. The MFCC feature vector has the most features that are extracted giving the reason for the high accuracy. However, this accuracy decreases when the recorded word is three syllables or longer. The system proposed has proven to be a valid method that can detect SLI.
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References
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Publishing (2013)
Grimm, A., Schulz, P.: Specific language impairment and early second language acquisition: the risk of over-and underdiagnosis. Child Ind. Res. 7(4), 821–841 (2014). https://doi.org/10.1007/s12187-013-9230-6
Miriam Webster: Timbre. https://www.merriam-webster.com/dictionary/timbre. Accessed 25 Nov 2019
Klapuri, A.: Signal Processing Methods for Music Transcription. Springer, Boston (2006). https://doi.org/10.1007/0-387-32845-9
American Speech-Language-Hearing Association: Spoken Language Disorders. https://www.asha.org/PRPSpecificTopic.aspx?folderid=8589935327§ion=Assessment. Accessed 10 Sept 2019
Grill, P., Tučková, J.: Speech databases of typical children and children with SLI. PLoS One 11(3), 1–21 (2016)
Georgopoulos, V.C., Malandraki, G.A., Stylios, C.D.: Development of intelligent method for differential diagnosis of specific language impairment. In: Proceedings of the 23rd Annual EMBS International Conference. IEEE, Istanbul (2001)
Yeo, C.Y., Al-Haddad, S.A.R, Ng, C.K.: Animal voice recognition for identification (id) detection system. In: 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, pp. 198–201. IEEE (2011)
Kumar, A.N.A., Muthukumaraswamy, S.A.: Text dependent voice recognition system using MFCC and VQ for security applications. In: International Conference on Electronics, Communication and Aerospace Technology, pp. 130–136. IEEE, Coimbatore (2017)
Liu, J., et al.: Bowel sound detection based on MFCC feature and LSTM neural network. In: 2018 IEEE Biomedical Circuits and Systems Conference (BIOCAS). IEEE, Cleveland (2018)
Korba, M.C.A., Bourouba, H., Rafik, D.: Text-independent speaker identification by combining MFCC and MVA features. In: 2018 International Conference on Signal, Image, Vision and Their Applications (SIVA). IEEE, Guelma (2018)
Statistica Help: Support Vector Machines Introductory Overview. https://documentation.statsoft.com/STATISTICAHelp.aspx?path=MachineLearning/MachineLearning/Overviews/SupportVectorMachinesIntroductoryOverview. Accessed 05 Oct 2019
Random Forest Classifier. https://www.globalsoftwaresupport.com/random-forest-classifier/. Accessed 05 Oct 2019
Recurrent Neural Networks. https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/recurrent_neural_networks.html. Accessed 05 Oct 2019
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Slogrove, K.J., van der Haar, D. (2020). Specific Language Impairment Detection Through Voice Analysis. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_10
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