One-dimensional convolutional neural network and hybrid deep-learning paradigm for classification of specific language impaired children using their speech. (January 2022) - Sharma, Yogesh; Singh, Bikesh Kumar
Highlights: Proposed 1D CNN and hybrid (CNN-LSTM) models to classify typical and SLI children. Automated-features from CNN were used instead of manually extracted features. Performance of customized CNN model improves with input data size. The hybrid model illustrated superior performance compared to CNN alone. Abstract: Background and objective: Screening children for communicational disorders such as specific language impairment (SLI) is always challenging as it requires clinicians to follow a series of steps to evaluate the subjects. Artificial intelligence and computer-aided diagnosis have supported health professionals in making swift and error-free decisions about the neurodevelopmental state of children vis-à-vis language comprehension and production. Past studies have claimed that typical developing (TD) and SLI children show distinct vocal characteristics that can serve as discriminating facets between them. The objective of this study is to group children in SLI or TD categories by processing their raw speech signals using two proposed approaches: a customized convolutional neural network (CNN) model and a hybrid deep-learning framework where CNN is combined with long-short-term-memory (LSTM). Method: We considered a publicly available speech database of SLI and typical children of Czech accents for this study. The convolution filters in both the proposed CNN and hybrid models (CNN-LSTM) estimated fuzzy-automated features from the speech utterance. We performed the experiments in five separate sessions. Data augmentations were performed in each of those sessions to enhance the training strength. Results: Our hybrid model exhibited a perfect 100% accuracy and F-measure for almost all the session-trials compared to CNN alone which achieved an average accuracy close to 90% and F-measure ≥ 92%. The models have further illustrated their robust classification essences by securing values of reliability indexes over 90%. Conclusion: The results confirm the effectiveness of proposed approaches for the detection of SLI in children using their raw speech signals. Both the models do not require any dedicated feature extraction unit for their operations. The models may also be suitable for screening SLI and other neurodevelopmental disorders in children of different linguistic accents.