A Hybrid Bald Eagle-Crow Search Algorithm for Gaussian mixture model optimisation in the speaker verification framework

Abstract

A mutualism of nature-inspired algorithms, the Bald Eagle Search Algorithm (BESA) with the Crow Search Algorithm (CSA), is proposed for Gaussian Mixture Model optimisation in the speaker verification framework. BESA simulates bald eagles’ hunting behaviour, whereas CSA simulates crows’ food-hiding behaviour. BESA excels in exploitation but has limitations in exploration, whereas CSA showcases strong exploration capabilities but faces challenges in effective exploitation. BESA’s hunting characteristic combines crows’ memory update to generate a Hybrid Bald Eagle-Crow Search Algorithm (HBE-CSA). The ability of the proposed HBE-CSA is tested on clean speech utterances from the Librispeech dataset and eight real-life noisy conditions from the AURORA dataset at four different Signal-to-Noise Ratios (SNRs). The comparative analysis shows that the proposed HBE-CSA converges with higher log-likelihood than individual search algorithms and the state-of-the-art Expectation Maximisation algorithm. Detection error trade-off curves validate the results of convergence behaviour, indicating that best speaker models are built using the proposed HBE-CSA. The noise analysis shows the smallest equal error rate for the proposed HBE-CSA at different SNRs than competing algorithms. The proposed HBE-CSA holds promise for optimising models not only in speaker verification but also across other areas of speech processing.