SILENT SPEECH SENTENCE RECOGNITION WITH SIX-AXIS ACCELEROMETERS USING CONFORMER AND CTC ALGORITHM

Yudong Xie

ICASSP 2026poster0 citations

SILENT SPEECH SENTENCE RECOGNITION WITH SIX-AXIS ACCELEROMETERS USING CONFORMER AND CTC ALGORITHM

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.

BibTeX

@inproceedings{icassp2026_silentspeechsent,
  title = {SILENT SPEECH SENTENCE RECOGNITION WITH SIX-AXIS ACCELEROMETERS USING CONFORMER AND CTC ALGORITHM},
  author = {Yudong Xie},
  booktitle = {ICASSP 2026},
  year = {2026}
}