A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
Sound
2023-08-28 v1 Machine Learning
Audio and Speech Processing
Quantum Physics
Abstract
We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum space to design kernel-based classifiers. Experimental results on challenging spoken command recognition tasks for a few low-resource languages, such as Arabic, Georgian, Chuvash, and Lithuanian, show that the proposed QKL-based hybrid approach attains good improvements over existing classical and quantum solutions.
Keywords
Cite
@article{arxiv.2211.01263,
title = {A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition},
author = {Chao-Han Huck Yang and Bo Li and Yu Zhang and Nanxin Chen and Tara N. Sainath and Sabato Marco Siniscalchi and Chin-Hui Lee},
journal= {arXiv preprint arXiv:2211.01263},
year = {2023}
}
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Submitted to ICASSP 2023