Silent Speech Sentence Recognition with Six-Axis Accelerometers using Conformer and CTC Algorithm

TL;DR

This paper introduces a novel silent speech recognition method using six-axis accelerometers combined with a Conformer and CTC algorithm, achieving high accuracy in converting facial motion signals into sentences for individuals with communication impairments.

Contribution

The study presents a new approach integrating accelerometer data with advanced neural networks for silent speech recognition, outperforming existing methods in accuracy.

Findings

Achieved 97.17% sentence recognition accuracy

Surpassed typical accuracy range of 85%-95%

Demonstrated accelerometers as effective SSI modality

Abstract

Silent speech interfaces (SSI) are being actively developed to assist individuals with communication impairments who have long suffered from daily hardships and a reduced quality of life. However, silent sentences are difficult to segment and recognize due to elision and linking. A novel silent speech sentence recognition method is proposed to convert the facial motion signals collected by six-axis accelerometers into transcribed words and sentences. A Conformer-based neural network with the Connectionist-Temporal-Classification algorithm is used to gain contextual understanding and translate the non-acoustic signals into words sequences, solely requesting the constituent words in the database. Test results show that the proposed method achieves a 97.17% accuracy in sentence recognition, surpassing the existing silent speech recognition methods with a typical accuracy of 85%-95%, and demonstrating the potential of accelerometers as an available SSI modality for high-accuracy silent speech sentence recognition.