Detection of Specific Language Impairment in Children Using Glottal Source Features

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.