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Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection
An article published in IEEE Access, Vol. 10, 2022
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE Access
2023
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| _version_ | 1867613138370691072 |
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| access_status_str | Open Access |
| author | Teye, Martha T. Missah, Yaw Marfo Ahene, Emmanuel Twum, Frimpong |
| author2 | 0000-0002-2370-4700 |
| author_browse | 0000-0002-2370-4700 Ahene, Emmanuel Missah, Yaw Marfo Teye, Martha T. Twum, Frimpong |
| author_facet | 0000-0002-2370-4700 Teye, Martha T. Missah, Yaw Marfo Ahene, Emmanuel Twum, Frimpong |
| author_sort | Teye, Martha T. |
| collection | Thesis |
| description | An article published in IEEE Access, Vol. 10, 2022 |
| format | Article |
| id | oai:ir.knust.edu.gh:123456789/14671 |
| institution | KNUST (Ghana) |
| language | English |
| last_indexed | 2026-06-10T12:31:22.621Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from KNUSTSpace — Kwame Nkrumah University of Science & Technology (Ghana) |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IEEE Access |
| publisherStr | IEEE Access |
| record_format | dspace |
| source_str | KNUSTSpace — Kwame Nkrumah University of Science & Technology (Ghana) |
| spelling | oai:ir.knust.edu.gh:123456789/14671 Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection Teye, Martha T. Missah, Yaw Marfo Ahene, Emmanuel Twum, Frimpong 0000-0002-2370-4700 0000-0002-2926-681X 0000-0002-0810-1055 0000-0002-1869-7542 An article published in IEEE Access, Vol. 10, 2022 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. KNUST 2023-12-06T14:23:30Z 2023-12-06T14:23:30Z 2022-02 Article IEEE Access, Vol. 10, 2022 10.1109/ACCESS.2022.3153787 https://ir.knust.edu.gh/handle/123456789/14671 en application/pdf IEEE Access |
| spellingShingle | Teye, Martha T. Missah, Yaw Marfo Ahene, Emmanuel Twum, Frimpong Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection |
| title | Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection |
| title_full | Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection |
| title_fullStr | Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection |
| title_full_unstemmed | Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection |
| title_short | Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection |
| title_sort | evaluation of conversational agents understanding culture context and environment in emotion detection |
| url | https://ir.knust.edu.gh/handle/123456789/14671 |
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