Learning to collaborate: Can young children develop better communication strategies through collaboration with a more popular peer

Unpopular children are known to have poor communication skills and experience difficulty in collaborative situations. This study investigated whether pairing unpopular, 5 to 6 year-old, children with a more popular peer would promote more effective collaboration. The study also investigated differences in popular and unpopular children's verbal and non-verbal communication. Thirty-six girls and 36 boys were placed in one of 12 popular, 12 unpopular or 12 mixed pairs. There were no mixed gender pairs. Children were filmed playing a collaborative game. Collaboration in popular pairs was more successful and less disputational than in unpopular pairs. Boys in unpopular pairs broke the rules of the game more often, argued more and did not monitoring their partners' facial expressions effectively. With popular partners they argued less, were more likely to elaborate disagreements, looked at their partner for longer, smiled more and were more likely to offer him a small toy. Unpopular girls' interactions were not markedly disruptive but they clearly benefited from being paired with a child with good communication skills. Popular girls modified their behaviour to take into account an unpopular partner's need for support. These findings suggest that pairing popular and unpopular children may be a useful classroom organisation strategy

Speaker identification using multimodal neural networks and wavelet analysis

© 2014 The Authors. Published by IET. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1049/iet-bmt.2014.0011The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content. In this study, the authors designed and implemented a novel text-independent multimodal speaker identification system based on wavelet analysis and neural networks. Wavelet analysis comprises discrete wavelet transform, wavelet packet transform, wavelet sub-band coding and Mel-frequency cepstral coefficients (MFCCs). The learning module comprises general regressive, probabilistic and radial basis function neural networks, forming decisions through a majority voting scheme. The system was found to be competitive and it improved the identification rate by 15% as compared with the classical MFCC. In addition, it reduced the identification time by 40% as compared with the back-propagation neural network, Gaussian mixture model and principal component analysis. Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable to real-time, text-independent speaker identification systems