Proposing a hybrid approach for emotion classification using audio and video data

Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%

Reducing Audible Spectral Discontinuities

In this paper, a common problem in diphone synthesis is discussed, viz., the occurrence of audible discontinuities at diphone boundaries. Informal observations show that spectral mismatch is most likely the cause of this phenomenon.We first set out to find an objective spectral measure for discontinuity. To this end, several spectral distance measures are related to the results of a listening experiment. Then, we studied the feasibility of extending the diphone database with context-sensitive diphones to reduce the occurrence of audible discontinuities. The number of additional diphones is limited by clustering consonant contexts that have a similar effect on the surrounding vowels on the basis of the best performing distance measure. A listening experiment has shown that the addition of these context-sensitive diphones significantly reduces the amount of audible discontinuities

Computing phonological generalization over real speech exemplars

Though it has attracted growing attention from phonologists and phoneticians Exemplar Theory (e g Bybee 2001) has hitherto lacked an explicit production model that can apply to speech signals An adequate model must be able to generalize but this presents the problem of how to generate an output that generalizes over a collection of unique variable-length signals Rather than resorting to a priori phonological units such as phones we adopt a dynamic programming approach using an optimization criterion that is sensitive to the frequency of similar subsequences within other exemplars the Phonological Exemplar-Based Learning System We show that PEBLS displays pattern-entrenchment behaviour central to Exemplar Theory s account of phonologization (C) 2010 Elsevier Ltd All rights reserve