Exploiting A Quantum Multiple Kernel Learning Approach For Low-Resource Spoken Command Recognition

We propose a theoretical analysis of quantum projection learning (QPL) that employs multiple kernels, highlighting its advantages through representation error analysis. Building upon previous studies that utilized a single quantum kernel-based method, we further investigate a quantum projection framework that incorporates multiple Gaussian kernels for low-resource spoken command recognition. Our empirical results align with our theoretical insights, suggesting that methods based on multiple kernels can further enhance the performance of QPL. By leveraging the quantum-to-classical projected output embeddings, we integrate this with a prototypical network for acoustic modeling. When evaluated using Arabic, Chuvash, Irish, and Lithuanian low-resource speech from CommonVoice, our proposed method surpasses the recurrent neural network and single kernel-based classifier baselines by an average of +5.28%.