A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
This addresses data sparsity in acoustic modeling for low-resource languages, but it appears incremental as a hybrid approach building on quantum techniques.
The authors tackled data sparsity in training acoustic models for low-resource spoken command recognition by proposing a quantum kernel learning framework, achieving good improvements over existing classical and quantum solutions on tasks in languages like Arabic and Georgian.
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.