Abstract
Speech disorders may affect the process of phonetic transcriptions. In the Automated Phonetic Transcription-the grading tool (APTgt), a linguistic E-learning system, to reduce the influence of disordered speech in the phonetic exams, we proposed a speech disorders classification module that aims to classify disordered speech and non-disordered speech. The Mel-frequency cepstral coefficients (MFCCs) are utilized to represent the features of the speech sound files. With the two different formats of MFCCs, we adopted two approaches to classifying the MFCCs: calculating the similarity between MFCC values by dynamic time warping (DTW) algorithm and classifying the distances by support vector machine (SVM); directly image classification by the convolutional neural network (CNN). We will focus on the second approach in this paper.
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Liu, J. et al. (2022). Speech Disorders Classification by CNN in Phonetic E-Learning System. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2022. Lecture Notes in Computer Science(), vol 13336. Springer, Cham. https://doi.org/10.1007/978-3-031-05643-7_36
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DOI: https://doi.org/10.1007/978-3-031-05643-7_36
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