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This paper introduces an emotion prediction model tailored for Black African society, addressing the impact of cultural context on conversational AI. The model combines speech and image data, utilizing a 3-layer CNN and a novel Audio-Frame Mean Expression (AFME) algorithm, to detect basic emotions and sarcasm. Achieving accuracies between 85% and 96%, the research highlights the importance of considering cultural nuances for ethical and effective emotion recognition in AI.
Ignoring cultural context in emotion detection could lead to AI systems that misinterpret or misunderstand emotional cues in diverse populations.
Valuable decisions and highly prioritized analysis now depend on applications such as facial biometrics, social media photo tagging, and human robots interactions. However, the ability to successfully deploy such applications is based on their efficiencies on tested use cases taking into consideration possible edge cases. Over the years, lots of generalized solutions have been implemented to mimic human emotions including sarcasm. However, factors such as geographical location or cultural difference have not been explored fully amidst its relevance in resolving ethical issues and improving conversational AI (Artificial Intelligence). In this paper, we seek to address the potential challenges in the usage of conversational AI within Black African society. We develop an emotion prediction model with accuracies ranging between 85% and 96%. Our model combines both speech and image data to detect the seven basic emotions with a focus on also identifying sarcasm. It uses 3-layers of the Convolutional Neural Network in addition to a new Audio-Frame Mean Expression (AFME) algorithm and focuses on model pre-processing and post-processing stages. In the end, our proposed solution contributes to maintaining the credibility of an emotion recognition system in conversational AIs.