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

Author:

Yang Chao-Han Huck1,Li Bo2,Zhang Yu2,Chen Nanxin2,Sainath Tara N.2,Marco Siniscalchi Sabato1,Lee Chin-Hui1

Affiliation:

1. Georgia Institute of Technology,USA

2. Google,USA

Publisher

IEEE

Reference41 articles.

1. An initialization strategy for addressing barren plateaus in parametrized quantum circuits

2. Common voice: A massively-multilingual speech corpus;ardila;Proceedings of the 12th Language Resources and Evaluation Conference,2020

3. Supervised quantum machine learning models are kernel methods;schuld,2021

4. Generalized End-to-End Loss for Speaker Verification

5. Power of data in quantum machine learning

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Exploiting A Quantum Multiple Kernel Learning Approach For Low-Resource Spoken Command Recognition;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. IIRI-Net: An interpretable convolutional front-end inspired by IIR filters for speaker identification;Neurocomputing;2023-11

3. Enhancing Privacy Preservation with Quantum Computing for Word-Level Audio-Visual Speech Recognition;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31

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