Enhancing Emotion Classification in Malayalam Accented Speech: An In-Depth Clustering Approach
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Abstract
Accurate emotion classification in accented speech for the Malayalam language poses a unique challenge in the realm of speech recognition. In this study, we explore the application of various clustering algorithms to this specific dataset, evaluating their effectiveness using the Silhouette Score as a measure of cluster quality. Our findings reveal significant insights into the performance of these algorithms. Among the clustering methods, Affinity Propagation emerged as the frontrunner, achieving the highest Silhouette Score of 0.5255. This result indicates a superior cluster quality characterized by well-defined and distinct groups. OPTICS and Mean Shift Clustering also demonstrated strong performance with scores of 0.4029 and 0.2511, respectively, indicating the presence of relatively distinct and well-formed clusters. In addition, we introduced Ensemble Clustering (Majority Voting), which achieved a score of 0.2399, indicating moderate cluster distinction. These findings provide a valuable perspective on the potential advantages of ensemble methods in this context. Our experiment results shed light on the effectiveness of various clustering methods in the context of emotion classification in accented Malayalam speech. This study contributes to the advancement of speech recognition technology and lays the groundwork for further research in this area..
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