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

arXiv 2211.01263 ↗First posted 2 Nov 2022cs.SD cs.LG eess.AScited by 0

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Abstract (arXiv)

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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References · 21

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  2. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  3. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
  4. 2018Supervised learning with quantum enhanced feature spacescited by 665
  5. 2018Barren plateaus in quantum neural network training landscapescited by 630
  6. 2020The power of quantum neural networkscited by 379
  7. 2020Power of data in quantum machine learningcited by 338
  8. 2017Generalized End-to-End Loss for Speaker Verificationcited by 293
  9. 2021Generalization in quantum machine learning from few training datacited by 216
  10. 2021Supervised quantum machine learning models are kernel methodscited by 200
  11. 2020In defence of metric learning for speaker recognitioncited by 126
  12. 2018Continuous-variable quantum neural networkscited by 74
9 more
  1. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  2. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  3. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 57
  4. 2018A neural attention model for speech command recognitioncited by 45
  5. 2021Speech-language Pre-training for End-to-end Spoken Language Understandingcited by 27
  6. 2021Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networkscited by 19
  7. 2020Improving End-to-End Speech-to-Intent Classification with Reptilecited by 17
  8. 2020Metric Learning for Keyword Spottingcited by 9
  9. 2017Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Kerascited by 6

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