Abstract
In this paper we evaluate the relevance of a perceptual spectral model for automatic motherese detection. We investigated various classification techniques (Gaussian Mixture Models, Support Vector Machines, Neural network, k-nearest neighbors) often used in emotion recognition. Classification experiments were carried out with short manually pre-segmented speech and motherese segments extracted from family home movies (with a mean duration of approximately 3s). Accuracy of around 86% were obtained when tested on speaker-independent speech data and 87.5% in the last study with speaker-dependent. We found that GMM trained with spectral feature MFCC gives the best score since it outperforms all the single classifiers. We also found that a fusion between classifiers that use spectral features and classifiers that use prosodic information usually increases the performance for discrimination between motherese and normal-directed speech (around 86% accuracy).
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Mahdhaoui, A., Chetouani, M., Kessous, L. (2010). Time-Frequency Features Extraction for Infant Directed Speech Discrimination. In: Solé-Casals, J., Zaiats, V. (eds) Advances in Nonlinear Speech Processing. NOLISP 2009. Lecture Notes in Computer Science(), vol 5933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11509-7_16
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DOI: https://doi.org/10.1007/978-3-642-11509-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11508-0
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