Kunuku, M. T., & Dehbozorgi, N. (2024, March), Enhanced Speech Recognition via A TensorFlow-Powered Lip Reading Model for Educational Applications  Paper presented at 2024 South East Section Meeting, Marietta, Georgia. 10.18260/1-2--45521

\bibitem{asee_peer_45521}
  Kunuku, M. T., \& Dehbozorgi, N. (2024, March), \emph{Enhanced Speech Recognition via A TensorFlow-Powered Lip Reading Model for Educational Applications}
  Paper presented at 2024 South East Section Meeting, Marietta, Georgia.
  10.18260/1-2--45521

Mourya Teja Kunuku and Nasrin Dehbozorgi.  "Enhanced Speech Recognition via A TensorFlow-Powered Lip Reading Model for Educational Applications".  2024 South East Section Meeting, Marietta, Georgia, 2024, March.  ASEE Conferences, 2024.  https://peer.asee.org/45521 Internet.  16 Dec, 2024

\bibitem{asee_peer_45521}
  Mourya Teja Kunuku and Nasrin Dehbozorgi.
  "Enhanced Speech Recognition via A TensorFlow-Powered Lip Reading Model for Educational Applications".
  \emph{2024 South East Section Meeting, Marietta, Georgia, 2024, March}.
  ASEE Conferences, 2024.
  https://peer.asee.org/45521 Internet.  16 Dec, 2024

@INPROCEEDINGS{asee_peer_45521
author = "Mourya Teja Kunuku and Nasrin Dehbozorgi"
title = "Enhanced Speech Recognition via A TensorFlow-Powered Lip Reading Model for Educational Applications"
booktitle = "2024 South East Section Meeting"
year = "2024"
month = "March"
address = "Marietta, Georgia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/45521}
number = {10.18260/1-2--45521}
}

TY  - CPAPER
AB  - Speech Recognition is a widely practiced technology and has a lot of applications in the academic domain and beyond. In educational research, AI-based speech recognition serves different purposes such as analysis of students’ team discussions, and classroom discourses, as well as assisting students with disabilities and hearing problems with transcriptions. However auditory speech recognition presents some challenges like environmental noise, poor audio quality, or even speaker identification in discourse analysis. This paper proposes an innovative approach to address these challenges by introducing a cutting-edge AI model for lip reading using Tensorflow. Our proposed model eliminates the need for auditory inputs in speech recognition, by utilizing artificial intelligence to analyze speech through visual cues of lip reading, also known as Visual Speech Recognition (VSR). The application of this novel method can significantly impact pedagogical practices.  By providing a real-time transcription of speech from lip-reading into text, it offers an advanced assistive learning tool for students with disabilities and greatly enhances knowledge accessibility. Furthermore, it empowers educational researchers to analyze video content even in environments with degraded audio quality, especially in remote learning settings.
AU  - Mourya Teja Kunuku
AU  - Nasrin Dehbozorgi
CY  - Marietta, Georgia
DA  - 2024/03/10
PB  - ASEE Conferences
TI  - Enhanced Speech Recognition via A TensorFlow-Powered Lip Reading Model for Educational Applications
UR  - https://peer.asee.org/45521
DO  - 10.18260/1-2--45521
ER  -