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Inductive conformal prediction for silent speech recognition.

Ming Zhang1, You Wang1, Wei Zhang1

  • 1State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, People's Republic of China.

Journal of Neural Engineering
|March 3, 2020
PubMed
Summary
This summary is machine-generated.

Inductive conformal prediction (ICP) enhances silent speech recognition by providing reliable predictions. Test time data augmentation further improves performance using unlabelled data with ICP.

Keywords:
guaranteed error rateinductive conformal predictionsilent speech recognitiontest time data augmentation

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Silent speech recognition using surface electromyography (sEMG) is an active research area.
  • Existing methods face challenges in providing confident and reliable predictions.
  • Feature selection and classification have seen progress, but prediction reliability remains a key issue.

Purpose of the Study:

  • To apply Inductive Conformal Prediction (ICP) for reliable silent speech recognition.
  • To introduce test time data augmentation to leverage unlabelled data for improved ICP performance.
  • To evaluate the effectiveness and validity of ICP in this application.

Main Methods:

  • Utilized Inductive Conformal Prediction (ICP) with a Random Forest algorithm.
  • Implemented test time data augmentation to incorporate unlabelled data.
  • Evaluated ICP performance across various significance levels on a custom sEMG dataset.

Main Results:

  • ICP generated p-values and confidence regions with guaranteed error rates for individual predictions.
  • Test time data augmentation demonstrated improved conformal prediction accuracy with increasing unlabelled data.
  • ICP's validity and efficiency were confirmed on a real-world silent speech recognition dataset.

Conclusions:

  • Inductive Conformal Prediction (ICP) is a viable and effective method for silent speech recognition.
  • ICP offers a powerful approach for confidence predictions, ensuring reliability in both data augmentation and online prediction scenarios.
  • The study highlights ICP's potential to significantly advance the field of reliable human-computer interaction.