Neural Network Architecture That Combines Temporal and Summative Features for Infant Cry Classification in the Interspeech 2018 Computational Paralinguistics Challenge

Abstract

This paper describes the application of a novel deep neural network architecture to the classification of infant vocalisations as part of the Interspeech 2018 Computational Paralinguistics Challenge. Previous approaches to infant cry classification have either applied a statistical classifier to summative features of the whole cry, or applied a syntactic pattern recognition technique to a temporal sequence of features. In this work we explore a deep neural network architecture that exploits both temporal and summative features to make a joint classification. The temporal input comprises centi-second frames of low-level signal features which are input to LSTM nodes, while the summative vector comprises a large set of statistical functionals of the same frames that are input to MLP nodes. The combined network is jointly optimized and evaluated using leave-one-speaker-out cross-validation on the challenge training set. Results are compared to independently-trained temporal and summative networks and to a baseline SVM classifier. The combined model outperforms the other models and the challenge baseline on the training set. While problems remain in finding the best configuration and training protocol for such networks, the approach seems promising for future signal classification tasks