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Automatic Identification of Articulation Disorders for Arabic Children Speakers
Workshop on Child Computer Interaction
Automatic identification of speech disorders in children's speech is very important for the diagnosis and monitoring of speech therapy. In this work, acoustic features (MFCC) have been used with the two most commonly used classification techniques in the speaker and language identification area, GMM-UBM and I-vector, for identifying three error types of speech production associated with phoneme [r] from Arabic children's speech. The sound [r] has been selected as it is the most commondoi:10.21437/wocci.2016-6 dblp:conf/wocci/HananiAFJHT16 fatcat:gqyrswrc7bexpcoxcbywteedt4