Asian Journal of Computer Science and Technology (AJCST)
Speech Recognition using Cross Correlation Algorithm Intended for Noise ReductionAuthor : Gagandeep Kaur and Seema Baghla
Volume 7 No.3 October-December 2018 pp 48-52
Biometrics is presently a buzzword in the domain of information security as it provides high degree of accuracy in identifying an individual. Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Rudimentary speech recognition software has a limited vocabulary of words and phrases, and it may only identify these if they are spoken very clearly. The research work is intended to build a GUI environment which would provide provisions to record the speech and would assist in multiplying the database. The research work is primarily focused to implement a system capable of recognizing a user’s speech and creating audio files that can be added up to create a dynamic template or database. The research work emphasizes on directly recording the spoken words avoiding the problems with use of microphone. On appropriate recording and removal of the noise, the best matched audio file from the template is recognized when an input is provided externally on the basis of graphs created by considering correlation.
Noise, speech recognition, cross correlation, biometrics, and spoken words.
 A. Pramanik, R. Raha, “Automatic speech recognition using correlation analysis”, World Congress on Information and Communication Technologies, pp. 670-6742018,.
 A. Shajee, D. Patel, R. Mishra, H. Saikia, M. Narendran, “A survey: Speech recognition application”, International Journal of Advances in Electronics and Computer Science, Vol. 4, No. 11, pp. 56-59, 2017.
 C. H. Wu, M. H. Su, W. B. Liang, “Miscommunication handling in spoken dialog systems based on error-aware dialog state detection”, Journal on Audio, Speech and Music processing, Vol. 9, pp. 1-17, 2017.
 C. Y. Chiang, “A parametric prosody coding approach for Mandarin speech using a hierarchical prosodic model”, Journal on Audio, Speech and Music processing, Vol. 5, pp. 1-24, 2018.
 G. Disken, Z. Tufekci, U. Cevik, “A robust polynomial regression-based voice activity detector for speaker verification”, Journal on Audio, Speech and Music processing, Vol. 23, pp. 1-16, 2017.
 G. Farahani, “Robust feature extraction using autocorrelation domain for noisy speech recognition”, Signal & Image Processing: An International Journal, Vol. 8, No. 1, pp. 23-44, 2017.
 H. Zhang, S. Warisawa, I. Yamada, “An Approach for Emotion Recognition using Purely Segment-Level Acoustic Features”, International Conference on Kansei Engineering and Emotion Research, pp. 1-11, 2014.
 J.V. Doremalen, C. Cucchiarini, H. Strik, “Optimizing automatic speech recognition for low-proficient non-native speakers”, Journal on Audio, Speech and Music Processing, Vol. 22, pp. 1-13, 2009.
 M. Himanshu, S. Kaur, V. Chaudhary, “Literature survey on automatic speech recognition system”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, No. 7, pp. 398-402, 2014.
 M. A. Hasan, “Correlation based fundamental frequency extraction method in noisy speech signal”, International Journal of Computer Science, Engineering and Information Technology, Vol. 7, No. 1, pp. 1-12, 2017.
 N. K. Saini, A. M. Laxmi, N. Balai, “Data extraction from web using speech recognition”, International Journal for Scientific Research and Development, Vol. 5, No. 11, pp. 175-177, 2018.
 M. Stern, C. Kim, A.R. Moghimi, A. Menon, “Binaural technology and automatic speech recognition”, International Congress of Acoustics, pp. 1-10, 2016.
 S. Elavarasi, G. Suseendran, “Speech Recognition on Handling Device”, Jour of Adv Research in Dynamical & Control Systems, Vol. 9, No. 6, pp. 97-103, 2017.
 S. Shankaranand, S. Manasa, M. Sharma, A.S. Nithya, K.S. Roopa, K.V. Ramakrishan, “An enhanced speech recognition system”, International Journal of Recent Development in Engineering and Technology, Vol. 2, No. 3, pp. 78-81, 2014.