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

Xianyan Fu; Xiao-Lei Zhang; Chao-Han Huck Yang; Jun Qi

ICASSP 2024accepted0 citations

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

Xianyan Fu, Xiao-Lei Zhang, Chao-Han Huck Yang, Jun Qi

Abstract

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%.

BibTeX

@inproceedings{icassp2024_exploitingaquant,
  title = {Exploiting A Quantum Multiple Kernel Learning Approach For Low-Resource Spoken Command Recognition},
  author = {Xianyan Fu and Xiao-Lei Zhang and Chao-Han Huck Yang and Jun Qi},
  booktitle = {ICASSP 2024},
  year = {2024}
}