Journal
NEUROSCIENCE RESEARCH
Volume 70, Issue 1, Pages 62-70Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.neures.2011.01.010
Keywords
MFCC; Classification; Motherese; Speech processing; Human interface; Speaker-independent emotion recognition systems
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Funding
- Research Institute of Science and Technology for Society (RISTEX)
- Japan Science and Technology Agency (JST)
- Nissan Science Foundation
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Infant-directed speech (IDS) has the important functions of capturing the infants' attention and maintaining communication between the mother and the infant. It is known that three acoustic components (F0, F0 range, and tempo) in IDS and adult-directed speech (ADS) are different. However, it is not easy to discriminate between IDS and ADS using procedural approaches due to the wide range of individual differences. In this paper, we propose a novel approach to discriminate between IDS and ADS that uses mel-frequency cepstral coefficient and a hidden Markov model-based speech discrimination algorithm; this approach is not based on the prosodic features of F0, F0 range, and tempo. The average discrimination accuracy of the proposed algorithm is 84.34%. The objective accuracy of the discrimination models have been confirmed using the head-turn preference procedure, which measures infants' listening duration to auditory stimuli of IDS and ADS. These results suggest that the proposed algorithm may enable a robust and reliable classification of mothers' speech and infant attention to the mothers' speech may depend on IDS clarity. (C) 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.
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