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.

客观。基于表面肌电图的无声语音识别已经研究了多年。尽管在特征选择和分类方面取得了一些进展，但仍然存在一个主要问题：如何提供自信或可靠的预测。方法。归纳保形预测（ICP）是解决这个问题的一种合适且有效的方法。本文将 ICP 与随机森林的底层算法一起应用，以提供置信度和可靠性。我们还提出了一种方法，即测试时间数据增强，以使用 ICP 作为利用未标记数据的一种方式，以提高预测性能。主要结果. 使用 ICP，以保证错误率获得单个预测的 p 值和置信区域。随着更多未标记的训练数据的积累，测试时间数据增强也会输出相对更好的保形预测。此外，在我们自己的设备获得的无声语音识别数据集上，证明和评估了不同显着性水平下 ICP 的有效性和效率。意义。这些结果表明ICP在无声语音识别中的可行性和有效性。此外，ICP 有可能成为一种强大的置信度预测方法，以确保数据增强和在线预测的可靠性。