In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

----The task of recognizing 3D gesture for controlling equipment is highly challenging due to the propagation of 3D smart TV recently. In this paper, the Ada-Boost algorithm is applied to 3D gesture recognition by using a Kinect sensor. We recognized time-invariant 3D gesture using global and local feature vectors that are normalized. The multi Ada-Boost algorithm is used to train and classify many 3D gesture types. Our experiment shows 95.17% of accuracy and 3.73% of error rate.