Motherese detection based on segmental and supra-segmental features

In this paper, we present an automatic motherese detection system for the study of parent-infant interaction analysis. Motherese is a speech register directed towards infants and it is characterized by higher pitch, slower tempo, and exaggerated intonation. The goal of this paper is to propose and evaluate different approaches for the detection of motherese from home movies. We investigated the characterization by supra-segmental features (prosody) but also by segmental ones namely the MFCC (Mel Frequency Cepstral Coefficients). Concerning the classification stage, we investigated two different methods: the k-nn (k-nearest neighbors) and the GMM (Gaussian Mixture Models). Experimental results show that segmental features play a major role on the detection.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Ammar Mahdhaoui, Mohamed Chetouani, Cong Zong
• Keywords: Automatic Motherese Detection, Computer Vision, Frequency Cepstral Coefficients, ICPR 2008, Parent-infant Interaction Analysis