Emotion Recognition from Speech using Prosodic and Linguistic Features

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dc.contributor.author Mahwish Pervaiz
dc.contributor.author Tamim Ahmed Khan
dc.date.accessioned 2018-12-14T06:07:57Z
dc.date.available 2018-12-14T06:07:57Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/123456789/8028
dc.description.abstract Speech signal can be used to extract emotions. However, it is pertinent to note that variability in speech signal can make emotion extraction a challenging task. There are a number of factors that indicate presence of emotions. Prosodic and temporal features have been used previously for the purpose of identifying emotions. Separately, prosodic/temporal and linguistic features of speech do not provide results with adequate accuracy. We can also find out emotions from linguistic features if we can identify contents. Therefore, We consider prosodic as well as temporal or linguistic features which help increasing accuracy of emotion recognition, which is our first contribution reported in this paper. We propose a two-step model for emotion recognition; we extract emotions based on prosodic features in the first step. We extract emotions from word segmentation combined with linguistic features in the second step. While performing our experiments, we prove that the classification mechanisms, if trained without considering age factor, do not help improving accuracy. We argue that the classifier should be based on the age group on which the actual emotion extraction be required, and this becomes our second contribution submitted in this paper. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.subject Department of Software Engineering en_US
dc.title Emotion Recognition from Speech using Prosodic and Linguistic Features en_US
dc.type Article en_US


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