A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

• URL: http://arxiv.org/abs/2211.01263v1
• Date: Wed, 2 Nov 2022 16:46:23 GMT
• Title: A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
• Authors: Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Tara N. Sainath, Sabato Marco Siniscalchi, Chin-Hui Lee
• Abstract summary: We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues. We project acoustic features based on classical-to-quantum feature encoding.
• Score: 69.97260364850001
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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