Speech recognition is an important component of biological identification which is an integrated technology of acoustics, signal processing and artificial intelligence. Recognition systems based on hidden Markov models are effective under particular circumstances, but do suffer from some major limitations that limit applicability of ASR technology in real-world environments. Attempts were made to overcome these limitations with the adoption of artificial neural networks as an alternative paradigm for ASR, but ANNs were unsuccessful in dealing with long time sequences of speech signals. So taking the limitations and advantages of both the systems it was proposed to combine HMM and ANN within a single, hybrid architecture. The goal in hybrid systems for ASR is to take advantage from the properties of both HMM and ANNs, improving flexibility and ASR performance For Speech recognition features from speech sample are extracted & mapping is done using Artificial Neural Networks. Multilayer pattern mapping neural network, which works on the principle of back propagation algorithm is proposed. Finally Speaker Recognition is done using Hidden Markov Model (HMM). The specialty of this model is the flexible and expandable hidden layer for recognition

1. Davis K. ,Biddulp,R. , and Balashek, S. ," Automatic Recognition of Spoken Digit," J. Acoust. Soc . Am. 24: Nov 1952,p. 637
2. H. F. Ong and A. M. Ahmad " Malay Language Speech Recogniser with Hybrid Hidden MarkovModel & Artificial Neural Network(HMM/ANN)" InternationalJournal of Information and Education Technology, Vol. 1, No. 2, June 2011.
3. NiladriSekharDey, RamakantaMohanty,K. L. Chugh"Speech & Speaker Recognition System using Artificial Neural Networks and Hidden Markov Model"2012 International Conference on Communication System and Network Technologies.
4. Surendra P. Ramteke, GunjalOza, Nilima P. Patil "Development of TTS for Marathi Speech Signal Based on Prosody and Concatenation Approach" International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 10, December- 2012
5. Li Bo, Wang Dong-xia, Zou De-jun, Hu Tie-sen"On Speech Recognition Access Control System Based on HMMI ANN"
6. R. Rojas " Neural Networks" Springer Verlag, Berlin,1996
7. "Hybrid Hidden Markov Model and Artificial Neural Network for Automatic Speech Recognition" Xian Tang , 2009 Pacific-Asia Conference on Circuits
8. A Comparative Study to Evaluate a Text-Independent Speaker Identification Engine for Arabic Speakers Using a CHMM-Based Approach
9. 'Voice command recognition based on MFCC and DTW' Anjali Bala et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7335-7342
10. "A Comparative Study of Filter Bank Spacing for Speech Recognition" Ben J. Shannon, Kuldip K. Paliwal Microelectronics Engineering Research Conference 2003
11. "Speech Recognition using MFCC" Chadawan Ittichaichareon, SiwatSuksri and Thaweesak YingthawornsukInternational Conference on Computer Graphics, Simulation and Modeling (ICGSM'2012) July 28-29, 2012 Pattaya (Thailand)
12. "Speech recognition using Back Propagation Algorithm" 1991 TECNON IEEE Region 10 International Conference on Energy ,Comminication and Control.

Speech Recognition Artificial Neural Network Speaker Recognition Hidden Markov Model