Automatic Motherese Detection for Face-to-Face Interaction Analysis

Authors:
Ammar Mahdhaoui;Mohamed Chetouani;Cong Zong;Raquel Sofia Cassel;Catherine Saint-Georges;Marie-Christine Laznik;Sandra Maestro;Fabio Apicella;Filippo Muratori;David Cohen
Affiliations:
Institut des Systèmes Intelligents et de Robotique, CNRS FRE 2507, Université Pierre et Marie Curie, Paris, France;Institut des Systèmes Intelligents et de Robotique, CNRS FRE 2507, Université Pierre et Marie Curie, Paris, France;Institut des Systèmes Intelligents et de Robotique, CNRS FRE 2507, Université Pierre et Marie Curie, Paris, France;Department of Child and Adolescent Psychiatry, AP-HP, Groupe Hospitalier Pitié-Salpétrière, Université Pierre et Marie Curie, Paris, France and Laboratoire Psychologie et Neuro ...;Department of Child and Adolescent Psychiatry, AP-HP, Groupe Hospitalier Pitié-Salpétrière, Université Pierre et Marie Curie, Paris, France and Laboratoire Psychologie et Neuro ...;Department of Child and Adolescent Psychiatry, Association Santé Mentale du 13ème, Paris, France;Scientific Institute Stella Maris, University of Pisa, Italy;Scientific Institute Stella Maris, University of Pisa, Italy;Scientific Institute Stella Maris, University of Pisa, Italy;Department of Child and Adolescent Psychiatry, AP-HP, Groupe Hospitalier Pitié-Salpétrière, Université Pierre et Marie Curie, Paris, France and Laboratoire Psychologie et Neuro ...
Venue:
Multimodal Signals: Cognitive and Algorithmic Issues
Year:
2009

Citing 4
Cited 1

Speaker identification and verification using Gaussian mixture speaker models

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An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech

Speech Communication

Time-Frequency features extraction for infant directed speech discrimination

NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing

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Abstract

This paper deals with emotional speech detection in home movies. In this study, we focus on infant-directed speech also called "motherese" which is characterized by higher pitch, slower tempo, and exaggerated intonation. In this work, we show the robustness of approaches to automatic discrimination between infant-directed speech and normal directed speech. Specifically, we estimate the generalization capability of two feature extraction schemes extracted from supra-segmental and segmental information. In addition, two machine learning approaches are considered: k-nearest neighbors (k-NN) and Gaussian mixture models (GMM). Evaluations are carried out on real-life databases: home movies of the first year of an infant.