TOPSIS driven complex picture fuzzy approach for speech matching and sports training feature recognition

Speech matching and sports training feature recognition have become increasingly significant in artificial intelligence and sports science, necessitating robust decision-making frameworks to address inherent uncertainty, hesitation, and cyclic behaviors in these domains. Current approaches to multi-criteria decision-making (MCDM) often fail to address uncertainties and interactions adequately. To overcome these limitations, this paper proposes the incorporation of complex picture fuzzy information measures (CPF-IM) to boost the accuracy of TOPSIS-based decision-making. Particularly, novel similarity measures (SMs) and distance measures (DMs) have been developed to cover real and imaginary components assigned to membership degree (MD), abstinence degree (AD), and non-membership degree (NDM) within a complex picture fuzzy set (CPFS). The evaluation method employs a real-world scenario in which four domain experts rated five speaker profiles under ten relevant criteria. Result outcomes indicate that the proposed model achieves consistent alternative rankings by detecting the interdependent relationships between acoustic and biomechanical parameters. The proposed CPF-TOPSIS approach surpasses other techniques in terms of accuracy and reliability, as evidenced by the results of comparative studies. The research establishes a new decision framework for speech and sports sciences, which enhances expert assessment decisions by accurately handling uncertain data, cyclical patterns, and evaluation hesitations.