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Quantum Optimization Quantum Machine Learning

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

arXiv
Authors: Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Tara N. Sainath, Sabato Marco Siniscalchi, Chin-Hui Lee

Year

2022

Paper ID

57747

Status

Preprint

Abstract Read

~2 min

Abstract Words

89

Citations

N/A

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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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • 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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2211.01263 [2] arXiv https://arxiv.org/abs/2211.01263 [3] Publisher https://arxiv.org/abs/2211.01263

Local Citation Graph (Related-Paper Links)

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