Evaluation Framework for Disabled Students based on Speech Recognition Technology

Full Text (PDF, 637KB), PP.10-17

Views: 0 Downloads: 0

Author(s)

Sanjay Kumar Pal 1,* Seemanta Bhowmick 1

1. Department of Computer Science and Applications, NSHM College of Management & Technology, Kolkata, 700053, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2017.10.02

Received: 6 Aug. 2017 / Revised: 26 Aug. 2017 / Accepted: 18 Sep. 2017 / Published: 8 Oct. 2017

Index Terms

Speech Recognition, Hidden Markov Model (HMM), Phoneme, Disabled Students, Evaluation, Framework

Abstract

This paper intends to develop an evaluation framework for the students with disabilities based on speech recognition technology. Education is the most significant ingredient in the development and empowerment of individuals. Till the last decade, education was provided to the persons with disabilities in segregated school settings or “special schools”. But in the recent years, there has been a great shift in societal attitude towards disabled students globally. The calls for “integration” of all students, disabled students and non–disabled students into the mainstream classroom environments have gathered momentum worldwide. In the pre–existing frameworks, the disabled students faced great difficulty while interacting with the system. The prime objective of our proposed framework is to provide a user–friendly and interactive environment that gives equal opportunities to all the students being evaluated. The utilization of speech recognition technology would lead to the elimination of all misinterpretations arising due to the human scribe or mediator and would enhance the ability of the disabled students to keep pace with the other students.

Cite This Paper

Sanjay Kumar Pal, Seemanta Bhowmick, "Evaluation Framework for Disabled Students based on Speech Recognition Technology", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.10, pp. 10-17, 2017. DOI:10.5815/ijmecs.2017.10.02

Reference

[1]B.H. Juang and Lawrence R. Rabiner, “Automatic Speech Recognition – A Brief History of the Technology Development”.
[2]Kirsi Jääskeläinen and Nina Nevala, “Use of Assistive Technology in Workplaces of Employees with Physical and Cognitive Disabilities”, K. Miesenberger et al. (Eds.): ICCHP 2012, Part I, LNCS 7382, pp. 223–226, 2012.
[3]Lawrence R. Rabiner, Jay G. Wilpon and Frank K. Soong, “High Performance Connected Digit Recognition Using Hidden Markov Models”, IEEE TRANSACTIONS ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, Vol. 37, Issue 8, August 1989.
[4]Shikha Gupta, Mr. Amit Pathak, Mr. Achal Saraf, “A STUDY ON SPEECH RECOGNITION SYSTEM : A LITERATURE REVIEW”, International Journal of Science, Engineering and Technology Research, Vol. 3, Issue 8, August 2014.
[5]B.H. Juang and Lawrence R. Rabiner, “Hidden Markov Models for Speech Recognition”, Technometrics, Vol. 33, No. 3, pp. 251–272, August 1991.
[6]Richard Schwartz, Chris Barry, Yen–Lu Chow et al., “The BBN BYBLOS Continuous Speech Recognition System.”
[7]Lawrence R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, PROCEEDINGS OF THE IEEE, Vol. 77, No. 2, pp. 21–27, February 1989.
[8]Suma Swamy and K.V. Ramakrishnan, “An Efficient Speech Recognition System”, Computer Science & Engineering Journal, Vol. 3, No. 4, August 2013.
[9]Fouzia Khursheed Ahmad, “Use of Assistive Technology in Inclusive Education: Making Room for Diverse Learning Needs”, Transcience, Vol. 6, Issue 2, pp. 62–77, 2015.
[10]Preeti Saini and Parneet Kaur, “Automatic Speech Recognition: A Review”, International Journal of Engineering Trends and Technology, Vol. 2, Issue 11, pp. 132–136, November 2014.
[11]Sumalatha. V and Dr. Santhi. R., “A Study on Hidden Markov Model (HMM)”, International Journal of Advance Research in Computer Science and Management Studies, Vol. 4, Issue 2, pp. 465–469, 2013.
[12]Suma Shankaranand, Manasa S, Mani Sharma, et al., “An Enhanced Speech Recognition System”, International Journal of Recent Development in Engineering and Technology, Vol. 2, Issue 3, pp. 78–81, March 2014.
[13]Satyanand Singh, Abhay Kumar, David Raju Kolluri, “Efficient Modelling Technique based Speaker Recognition under Limited Speech Data”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.8, No.11, pp.41–48, 2016. DOI: 10.5815/ijigsp.2016.11.06
[14]Anjali Pahwa, Gaurav Aggarwal, “Speech Feature Extraction for Gender Recognition”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.8, No.9, pp.17–25, 2016. DOI: 10.5815/ijigsp.2016.09.03
[15]Hajer Rahali, Zied Hajaiej, Noureddine Ellouze, “Robust Features for Speech Recognition using Temporal Filtering Technique in the Presence of Impulsive Noise”, IJIGSP, Vol.6, No.11, pp.17–24, 2014. DOI: 10.5815/ijigsp.2014.11.03
[16]Ravi Kumar. K, P.V. Subbaiah, “A Survey on Speech Enhancement Methodologies”, International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.12, pp.37–45, 2016. DOI: 10.5815/ijisa.2016.12.05
[17]Mary Thorpe, “Handbook of Education Technology”, Ellington, Percival and Race, 1988.