Journal Press India®

Empowering Communication and the Role of Speech Recognition in Accessibility

Vol 6 , Issue 1 , January - June 2023 | Pages: 33-42 | Research Paper

Author Details ( * ) denotes Corresponding author

1. * Tejasree Mankenapalli, Student, CSE , KL University, Vijayawada, Andhra Pradesh (New), India (

This research paper explores advancements in speech recognition technology. Speech recognition, a pivotal area of artificial intelligence, involves converting spoken language into text or commands. The paper delves into foundational techniques like Hidden Markov Models (HMMs) and their evolution into modern Deep Learning approaches. It discusses the challenges posed by variations in accents, languages, and background noise, and showcases the integration of large datasets and sophisticated neural architectures. The study also emphasizes real-time ap- plicability and improved human-machine interaction. Through this investigation, the paper contributes to the understanding of cutting-edge methods in speech recognition and their practical implications.


Speech processing, Speech recognition, Communication, Deep learning, CNN

  1. Lakkhanawannakun, P. (June 2019). Speech Recognition using Deep Learning.
  2. Sharma, R. E., Ahmad, T. & Alam, F. (June 2018). Emotion Analysis and Speech Signal Processing,
  3. Poorjam, A.H. (2019). Quality Control in Remote Speech Data Collection.
  4. Philipos C. Loizou Speech Quality Assessment, Vol 346
  5. Benkerzaz, S., Elmir, Y. & Dennai, A. (2019). A Study on Automatic Speech Recognition.
  6. Hu, Y. (2008). Evaluation of Objective Quality Measures for Speech Enhance- ment.
  7. Dimmita, N. & Siddaiah, P. (2019). Speech Recognition Using Convolutional Neural Network. audio
  8. Hossain, M. S. & Muhammad, G. (2019). Emotion recognition using deep learning approach from audio–visual emotional big data. Inf. Fusion, 49, 69–78.
  9. Chen, M., Zhou, P. & Fortino, G. (2016). Emotion communication system. IEEE Access, 5, 326–337.
  10. Lalitha, S., Madhavan, A., Bhushan, B. & Saketh, S. (2014). Speech emotion recognition. In Proc. Int. Conf. Adv. Electron. Comput. Commun. (ICAECC), 1–4.
  11. Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Sci. Inf., 44(4), 695–729.
  12. Koolagudi, S. G. & Rao, K. S. (2012). Emotion recognition from speech: A review. Int. J. speech Technol., 15(2), 99–117.
  13. Schmidhuber, J. (2015). Deep learning in neural networks: An overview.  Neural Netw., 61, 85–117.
  14. Demircan, S. & Kahramanlı, H. (2014). Feature extraction from speech data for emotion recognition. J. Adv. Comput. Netw., 2(1), 28–30.
Abstract Views: 1
PDF Views: 107

By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy.