A new approach for motherese detection using a semi-supervised algorithm

Authentic and natural infant-parent interactions analysis requires the development of efficient detectors such as the discrimination between infant and adult-directed speech. Supervised methods have been found to be efficient for labeled data. The annotation process is time-consuming and the eventual divergence between annotators increases the difficulty. Semi-supervised approaches such as co-training offers a framework allowing to take advantage of supervised classifiers trained by different features. The proposed motherese detector system combined various features and classifiers used in emotion recognition in a co-training framework. The results show the relevance of this approach for real-life corpora such as home movies.