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The advancements of the Internet of Things (IoT) and voice-based multimedia applications have resulted in the generation of big data consisting of patterns, trends and associations capturing and representing many features of human behaviour. The latent representations of many aspects and the basis of human behaviour is naturally embedded within the expression of emotions found in human speech. This signifies the importance of mining audio data collected from human conversations for extracting human emotion. Ability to capture and represent human emotions will be an important feature in next-generation artificial intelligence, with the expectation of closer interaction with humans. Although the textual representations of human conversations have shown promising results for the extraction of emotions, the acoustic feature-based emotion detection from audio still lags behind in terms of accuracy. This paper proposes a novel approach for feature extraction consisting of Bag-of-Audio-Words (BoAW) based feature embeddings for conversational audio data. A Recurrent Neural Network (RNN) based state-of-the-art emotion detection model is proposed that captures the conversation-context and individual party states when making real-time categorical emotion predictions. The performance of the proposed approach and the model is evaluated using two benchmark datasets along with an empirical evaluation on real-time prediction capability. The proposed approach reported 60.87% weighted accuracy and 60.97% unweighted accuracy for six basic emotions for IEMOCAP dataset, significantly outperforming current state-of-the-art models.

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