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.
Highlights
Developmental dysphasia (DD) or Specific Language Impairment (SLI) is a language disorder that delays the language development in children who have no hearing problems, neurological dysfunctions or other developmental delays [1]–[3]
Three sets of acoustic features and one set of glottal features are extracted for every speech utterance
Experimental results show that the glottal parameters resulted in good classification accuracies comparable to the ones obtained with acoustic features
Summary
Developmental dysphasia (DD) or Specific Language Impairment (SLI) is a language disorder that delays the language development in children who have no hearing problems, neurological dysfunctions or other developmental delays [1]–[3]. Children with SLI show poor speech motor skills [15], and as a result the nature of vocal-fold vibration is deviated compared to healthy speech This motivated us to use the features computed from the glottal waveform, which may have useful discriminating information, for SLI detection. The study focuses on two aspects: (i) Exploring the effectiveness of glottal parameters extracted from speech for detection of SLI in children, and (ii) Analyzing the robustness of different acoustic features for speaker-independent SLI classification. Experiments are carried out using the only publicly available SLI database [13] to systematically study the effectiveness of glottal parameters when used individually and combined with the reference openSMILE and MFCC features in the classification of children with SLI.
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