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Input voice data is considered from a large dataset and pre-processed using a Data normalization and adaptive bilinear filtering approach. Afterward, acoustic features are taken out from the voice signals to capture related information for emotion recognition. These features can include linear prediction coefficients (LPC), three-dimensional (3D) log-mel spectrum, mel-frequency cepstral coefficients (MFCCs), and Prosodic features. Subsequently, feature selection is performed using an improved wild horse optimization (WHO) approach. Finally, a hybrid capsule slime mould dense deep learning framework (HCSDN) is used for voice-based emotion recognition. IEMOCAP and EMODB datasets are used to calculate system performance. The performance metrics denote the proposed system achieves 96.78% accuracy, 96.45% specificity, 95.81% precision, 4.256% error rate, and 94.256% sensitivity, 0.75% false positive rate in terms of the IEMOCAP dataset. Similarly, the proposed system achieves 96.85% accuracy, 95.74% specificity, 96.12% precision, 3.432% error rate, 95.25% sensitivity, and 0.62% false positive rate in terms of the EMODB dataset. <\/jats:p>","DOI":"10.1142\/s0218001424500174","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T06:58:46Z","timestamp":1719644326000},"source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Voice-Based Emotion Recognition Using Hybrid Capsule Slime Mould Dense Deep Learning Framework"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8764-9982","authenticated-orcid":false,"given":"V. V. 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