Inductive conformal prediction for silent speech recognition

Abstract

Objective. Silent speech recognition based on surface electromyography has been studied for years. Though some progress in feature selection and classification has been achieved, one major problem remains: how to provide confident or reliable prediction.Approach. Inductive conformal prediction (ICP) is a suitable and effective method to tackle this problem. This paper applies ICP with the underlying algorithm of random forest to provide confidence and reliability. We also propose a method, test time data augmentation, to use ICP as a way to utilize unlabelled data in order to improve prediction performance.Main Results. Using ICP, p-values and confidence regions for individual predictions are obtained with a guaranteed error rate. Test time data augmentation also outputs relatively better conformal predictions as more unlabelled training data accumulated. Additionally, the validity and efficiency of ICP under different significance levels are demonstrated and evaluated on the silent speech recognition dataset obtained by our own device.Significance. These results show the viability and effectiveness of ICP in silent speech recognition. Moreover, ICP has potential to be a powerful method for confidence predictions to ensure reliability, both in data augmentation and online prediction.

Keywords: guaranteed error rate; inductive conformal prediction; silent speech recognition; test time data augmentation.