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An Analysis on Multimodal Framework for Silent Speech Recognition
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Author(s): Ramkumar Narayanaswamy (PSG College of Technology, India), Karthika Renuka D. (PSG College of Technology, India), Geetha S. (Vellore Institute of Technology, Chennai, India), Vidhyapriya R (PSG College of Technology, India)and Ashok Kumar L. (PSG College of Technology, India)
Copyright: 2023
Pages: 18
Source title:
Principles and Applications of Socio-Cognitive and Affective Computing
Source Author(s)/Editor(s): S. Geetha (Vellore Institute of Technology, Chennai, India), Karthika Renuka (PSG College of Technology, India), Asnath Victy Phamila (Vellore Institute of Technology, Chennai, India)and Karthikeyan N. (Syed Ammal Engineering College, India)
DOI: 10.4018/978-1-6684-3843-5.ch010
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Abstract
A brain-computer interface (BCI) is a computer-based system that collects, analyses, and converts brain signals into commands that are sent to an output device to perform a desired action. BCI is used as an assistive and adaptive technology to track the brain activity. A silent speech interface (SSI) is a system that enables speech communication when an acoustic signal is unavailable. An SSI creates a digital representation of speech by collecting sensor data from the human articulatory, their neural pathways, or the brain. The data from a single stage is very minimal in order to capture for further processing. Therefore, multiple modalities could be used; a more complete representation of the speech production model could be developed. The goal is to detect speech tokens from speech imagery and create a language model. The proposal consists of multiple modalities by taking inputs from various biosignal sensors. The main objective of the proposal is to develop a BCI-based end-to-end continuous speech recognition system.
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