Classification of children with voice impairments using deep neural networks

This paper presents the deep neural networks to classification of children with voice impairments from speech signals. In the analysis of speech signals, 6,373 static acoustic features are extracted from many kinds of low-level-descriptors and functionals. To reduce the variability of extracted features, two-dimensional normalizations are applied to smooth the interspeaker and inter-feature mismatch using the feature warping approach. Then, the feature selection is used to explore the discriminative and low-dimensional representation based on techniques of principal component analysis and linear discriminant analysis. In such representation, the robust features are obtained by eliminating noise features via subspace projection. Finally, the deep neural networks are adopted to classify the children with voice impairments. We conclude that deep neural networks with the proposed feature normalization and selection can significantly contribute to the robustness of recognition in practical application scenarios. We have achieved an UAR of 60.9% for the four-way diagnosis classification on the development set. This is a relative improvement of 16.2% to the official baseline by using our single system.