Detecting Developmental Dysphasia in Children using Speech Data

• Main Authors: Ramarao, D, Singh, Chaman, Shahnawazuddin, S., Adiga, Nagaraj, Pradhan, Gayadhar
• Format: Conference Proceeding
• Language: English
• Subjects: Developmental dysphasia; extreme learning machine; Feature extraction; Gaussian posteriograms; Mel frequency cepstral coefficient; Pediatrics; Training; Training data
• ISSN: 2474-915X
• DOI: 10.1109/SPCOM.2018.8724441

Developmental dysphasia or specific language impairment (SLI) is a disorder that is known to delay the process of acquiring language skills in children without other disabilities. Approximately 5-7% of children in kindergarten group are affected with SLI as reported in literature. Boys are more prone to be affected by this disorder compared to girls. In this paper, we present our preliminary attempts towards detecting SLI in children using their speech data. In this regard, we have used Mel-frequency cepstral coefficients (MFCC) for front-end speech parameterization. We have also presented an analysis to show how MFCC features help in discriminating the healthy children from those affected with SLI. The MFCC feature vectors are then used to develop two-class classifiers for discriminating healthy children from those suffering from SLI. The said two-class classifiers are developed using extreme learning machine (ELM) trained and tested on speech data collected from healthy children as well as those affected with SLI. ELM are fast to train and are known to be quite effective even when the training data is sparse. For extracting utterance-level features to be given as input to the ELM, Gaussian posteriograms learned on frame-level acoustic features are used. Several different types of ELMs are explored in this work and the kernel ELM is noted to outperform the rest with an accuracy of 99.41%.