![]() ![]() In terms of computational complexity, DFOFDM demonstrated effective scalability, providing a feasible solution for large-scale applications. When compared with a contemporary model, “Transfer Subspace Learning by Least Square Loss (TSLSL)”, DFOFDM displayed superior results across various evaluation metrics, indicating a promising improvement in the field of speech emotion recognition. Notably, the DFOFDM model showed resilience to label imbalances and noise in speech data, crucial for real-world applications. The F-Score, another crucial metric, also reflected comparable statistics for each label. Similar performance was observed in terms of recall, with most emotions falling within the 90% to 95% range. Emotion labels such as ‘Angry’, ‘Happy’, and ‘Neutral’ showed a precision rate over 92%, while other emotions fell within the range of 87% to 90%. The performance of the proposed DFOFDM approach is evaluated extensively. The study’s central method involves a Cuckoo Search-based classification strategy, which is tailored for this multi-label problem. This approach employs acoustic and spectral features from speech signals, coupled with an optimized feature selection process using a fusion of diversity measures. The paper begins by elucidating the necessity for improved emotion recognition methods, followed by a detailed introduction to DFOFDM. ![]() This article presents a comprehensive study and analysis of a novel approach, “Digital Features Optimization by Diversity Measure Fusion (DFOFDM)”, aimed at addressing these challenges. The task, however, presents significant challenges due to the high dimensionality and noisy nature of speech data. Ībstract: Emotion recognition from speech signals serves a crucial role in human-computer interaction and behavioral studies. ![]() Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India. Ashok Kumar Konduru, Research Scholar, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, IndiaĬorresponding author. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India Authors: Konduru, Ashok Kumar a * | Mazher Iqbal, J.L. ![]()
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