NDLI: Detection of Specific Language Impairment in Children Using Glottal Source Features

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Paavo Alku Krothapalli Sreenivasa Rao Mittapalle Kiran Reddy
Abstract	Developmental dysphasia, also known as specific language impairment (SLI), is a language disorder in children that involves difficulty in speaking and understanding spoken words. Detecting SLI at an early stage is very important for successful speech therapy in children. In this paper, we propose a novel approach based on glottal source features for detecting children with SLI using the speech signal. The proposed method utilizes time- and frequency-domain glottal parameters, which are extracted from the voice source signal obtained using glottal inverse filtering (GIF). In addition, Mel-frequency cepstral coefficient (MFCC) and openSMILE based acoustic features are also extracted from speech utterances. Two machine learning algorithms, namely, support vector machine (SVM) and feed-forward neural network (FFNN), are trained separately for the MFCC, openSMILE and glottal features. A leave-fourteen-speakers-out cross-validation strategy is used for evaluating the classifiers. The experiments are conducted using the SLI speech corpus launched by the LANNA research group. Experimental results show that the glottal parameters contain significant discriminative information required for identifying children with SLI. Furthermore, the complementary nature of glottal parameters is investigated by independently combining these features with the MFCC and openSMILE acoustic features. The overall results indicate that the glottal features when used in combination with MFCC feature set provides the best performance with the FFNN classifier in the speaker-independent scenario.
e-ISSN	21693536
DOI	10.1109/ACCESS.2020.2967224
Journal	IEEE Access
Volume Number	8
Language	English
Publisher	IEEE
Publisher Date	2020-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Developmental Dysphasia Opensmile Glottal Source Parameters Support Vector Machines Artificial Neural Networks
Content Type	Text
Resource Type	Article

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