A Speech Disorder Detection Model Using Ensemble Learning Approach

Abstract

Speech disorder detection (SDD) models can assist speech therapists in providing personalized treatment to individuals with speech impairment. Speech disorders (SDs) comprise a broad spectrum of problems that affect the production, articulation, fluency, and quality of speech. Prompt identification and timely intervention are essential for efficient control and therapy of speech problems. However, current diagnostic techniques often depend on subjective evaluations by speech-language pathologists and may encounter challenges in terms of accessibility, scalability, and consistency. The limited dataset and substantial computational power reduce the efforts for developing automated SDD models. However, recent technology developments enable researchers to determine key factors to classify voice samples. The voice sample classification can identify the severity of the SD. Ensemble learning (EL) facilitates the integration of multiple models’ predictions to generate an optimal outcome. Hence, an EL-based SDD model is introduced in this study. A mel-spectrogram (MS) generator is built to produce images using the voice samples. The authors suggested a feature engineering based on the ResNet 18 model for extracting crucial features from the MS. CatBoost and XGBoost models are employed to classify the features. The outcomes of these models are used to train the support vector machine model to make a final prediction. The VOice ICarfEDerico II (VOICED) dataset is used to generalize the proposed model. The experimental findings indicate that the recommended SDD model outperforms the state-of-the-art models by yielding an outstanding performance. This suggests that the model can assist speech therapists in offering customized speech therapies based on the SD severity. The model performance can be improved using multilanguage voice samples.