Learning Assistant in Educational Field Using Automatic Speech Recognition
PrajaktaKotwal , Prof.M.R.Dixit
Automatic Speech recognition is the translation of spoken words into text. It takes speech data as input and divides it into small time domain frames. Speech signal processing considering speech signals stationary for a small time interval. From point of view speech signals are divided into small units Morphims or Phonims. Any speech data can be sorted as word uttered followed by voice and silence intervals. Voice activity detection can be are employed to detect voiced and unvoiced part of speech. Speech processing consists of speech recognition, speech synthesis, speaker recognition, understanding of speech with reference to context, speech coding, speech enhancement, speech transmission, speech to text conversion & text to speech conversion etc. In general speech to text conversion system will convert input speech data to output text data. If the input speech data is inappropriate with some errors then there is a possibility to get incorrect output data. The proposed system contains options for correction of inappropriate input data so that the output text and speech data produce and pronounce is correct. The proposed system will be employed as learning assistance in educational field for students to learn correct pronunciation of words. The proposed system will also help tourists for conversation in local language.
 Nelson Morgan,”Deep and Wide: Multiple Layers in Automatic Speech Recognition”,IEEE Transactions on audio,speech and language processing,vol.20, no.1, January 2012.
ArchanaShende,Subhash Mishra, Shiv Kumar, ”Comparison of different parameters used in GMM based automatic speaker recognition”, International Journal of Soft Computing and Engineering (IJSCE) Volume-1, Issue-3, July 2011
Mohamad Adan AL - ALaoui, Lina AL-Kanj, JimmyAzar,and Elias Yaacoub ,”Speech recognition using Artificial Neural Networks and Hidden Markov Model”, IEEE multisciplinary engineering education magazine.vol.3, no.3.september 2008.
Chulhee Lee, Donghoon Hyun, Euisun Choi, Jinwook Go, and ChungyongLee, ”Optimizing Feature Extraction for Speech Recognition”,IEEE transactions on speech and audio processing, vol. 11, no. 1, january 2003.
Harry Printz and Isabel Trancoso ,”Editorail”, IEEE transactions on speech and audio processing, vol. 10, no. 8, november 2002.
 Alexandros Potamianos, Member, IEEE, and Petros Maragos, “Time-Frequency Distributions for Automatic Speech Recognition”, IEEE transactions on speech and audio processing, vol. 9, no. 3, march 2001.
 VibhaTiwari,”MFCC and its applications in speaker recognition”, International Journal on Emerging Technologies 1(1): 19-22(2010).
D.B.Paul,”Speech Recognition using Hidden Markov Model”,The Lincoln Laboratory Journal vol.3, no.1,1990.
Lawrence R.Rabiner, ”A-Tutorial on Hidden Markov Models and selected applications in speech recognition”,vol.77,no.2, Feb 1999.
Yang Liu,”Enriching Speech Recognition with Automatic Detection of Sentence Boundaries and Disfluencies”IEEE Transactions on audio, speech and language processing, vol. 14, Sept 2006
[PrajaktaKotwal, Prof.M.R.Dixit (2013), Learning Assistant in Educational Field Using Automatic Speech Recognition, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-1, Issue-2, Page No-51-53], (ISSN 2347 - 5552). www.ijircst.org
Research Candidate, Kolhapur Institute of Technology, Kolhapur, Maharashtra,India. (e-mail:- email@example.com)