Dr A N Mishra
|Name||Dr A N Mishra|
|Designation||Professor and Head|
|Department||Electronics and Communication Engineering|
|Specialization||Year of passing||University/Inst.||Remarks|
|Ph.D||Speech and Signal Processing||2013||Birla Institute of Technology , Mesra , Ranchi|
|M.Tech||Digital Communication||2006||UPTU, Lucknow|
|B.Tech||Electronics and Communication Engg||2000||Gulbarga University|
Research InterestsSignal Processing , Speech Processing and Image Processing
Teaching InterestsSignals and Systems , DSP , Network , Communication Engg.
Academic/Professional AffiliationsLMIETE , UACEE, MIAENG
Published Paper Details
|1||Deepti Shinghal, A.N. Mishra, Amit Saxena, “Design And Implementation Of Adiabatic Latch For Low Power Embedded Systems”, International Journal of Scientific Research and Management Studies, Vol. 2, Issue 4, 2016.
|2||Deepti Shinghal, A.N. Mishra, Amit Saxena, “ Low Power Architecture for ASIPs: Based on Adiabatic Switching Principles”, International Journal of Engineering Sciences and Emerging Technologies , Vol. 8, Issue 6, 2016.
|3||Sharmila, A N Mishra and Neeta Awasthy, “Audio Visual Hindi Speech Recognition Using HMM ”, International Journal of Information and Communication Technology- Inderscience, 2015 (In – Press).
|4||Tripti Sharma , A N Mishra and Arun Kumar Yadav, “ Lifetime Improvement of Wireless Sensor Network Using Coding and Adaptive Duty Cycle”, International Journal of Science , Engineering and Technology Research , Vol. 4, Issue 11, Nov. 2015.
|5||Megha Yadav, Usha Sharma and A N Mishra, “ Suitable Feature Extraction Technique for Hindi Digit Speech Recognition”, Advances in Computer Science and Information Technology, Vol. 2, No. 12, pp. 50-53, July- Sept 2015.
|6||Jyoti Guglani and A N Mishra, “Comparative Study of Feature Extraction Techniques for Punjabi Speech Recognition”, International Journal of Science, Technology & Management, Volume No.04, Issue No. 02, February 2015.
|7||A. N. Mishra, Mahesh Chandra, Astik Biswas and S. N. Sharan, “Hindi phoneme-viseme recognition from continuous speech”, Int. J. Signal and Imaging Systems Engineering- Inderscience, Vol. 6, No. 3, 2013.
|8||Sharmila, A. N. Mishra, Neeta Awasthy “Hybrid Features for Speaker Independent Hindi Digits Recognition Using HTK Toolkit”, International Journal of Scientific & Engineering Research, Volume 4, Issue 12, 2013.
|9||A. N. Mishra, Mahesh Chandra, Astik Biswas and S. N. Sharan, “Robust Features for Connected Hindi Digits Recognition”, Int. Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 4, No. 2, pp. 79–90, June, 2011.
|10||Sharmila Verma and A N Mishra, “Analysis of Speech Recognition Techniques on the
Hindi Speech Digits Database”, International Journal of Electronic Engineering Research, Volume 3, No. 3, pp. 321-327, 2011.
|11||A.N. Mishra, Astik Biswas and Mahesh Chandra, “Isolated Hindi Digits Recognition: A Comparative Study”, Int. Journal of Electronics and Communication Engineering, Vol. 3, No.1, pp. 229-238, (2010).
|12||Pawan Kumar, Astik Biswas, A .N. Mishra and Mahesh Chandra, “ Spoken Language Identification Using Hybrid Feature Extraction Methods”, Journal of Telecommunications, Volume 1, Issue 2, March 2010.
|13||Usha Sharma, S. Maheshkar and A N Mishra, “Study of Robust Feature Extraction Techniques for Speech Recognition System”, in Proc of International conference on futuristic trend in computational analysis and knowledge management (ABLAZE 2015).
|14||A. N. Mishra, M. C. Shrotriya and S. N. Sharan, “Comparative Wavelet, PLP and LPC Speech Recognition Techniques on the Hindi Speech Digits Database”, in Proc. of SPIE Vol. 7546, 2nd International Conference on Digital Image Processing, Singapore pp. 341-346, 26-28 Feb 2010.
|15||Mahesh Chandra, Astik Biswas, A. N. Mishra, S. N. Sharan and Omar Farooq, “Cepstral based features for Language Identification”, in Proc. International Symposium Frontiers of Research on Speech and Music, IIIT Gwalior, pp. 42-46, Dec 2009.|
|16||Jyoti Guglani, A.N.Mishra and A.K.Pandey,” Speech Recoginition using Linear Predictive Based Features for Punjabi Digits”, International conference FTICT, RKGIT, Ghaziabad, 2011.|
|17||A. N. Mishra, Shipra Mishra, Mahesh Chandra and S. N. Sharan, “Speech Recognition Using Linear Prediction Based Features”, in Proc. National Seminar on Devices, Circuits & Communication, BIT, Mesra, Ranchi, India, Vol. 1, pp. 115-118, Nov 2008.|
Workshop/Seminar/FDP/ Summer School (Attended/ Organized)
- Attended 15 days Short term course on Digital speech processing at IIT,Kharagpur.
- Participated in symposium on Telecom regularities organized by IIT Delhi.
- Attended workshop on” Teaching Methodology” at GLAITM, Mathura.
Executive Summary (About Self)
He has strong research background based on speech recognition, noise robust acoustic feature extraction, audio-visual speech recognition. Has rich experience of working in a team actively and collaboratively, and also strong motivation to advancement of speech technology research.
The continuous research on speech technology has helped him acquire unique skills and in-depth understanding on HMMs, MFCC, and Wavelets etc. He has extracted both audio and visual features. For example, a paper on Hindi phoneme-viseme recognition from continuous speech was published at Int. J. Signal and Imaging Systems Engineering- Inderscience in 2013. Another paper on hybrid features for speaker independent Hindi digits recognition using HTK toolkit was published in 2013. His recent work of audio visual based feature fusion approach for speech recognition was published in 2015. To summarize, he has published 11 journal papers and 5 International conferences including reputed ones like ICDIP and FRSM on speech processing area.
The long years of experience on using HTK, Matlab, Praat’s helped him to develop his skill on speech recognition area, thus he can give a series of in-depth talks on recent trends of speech recognition. Within HTK framework, he can use other features extracted from MATLAB. His audio visual based features enhanced the performance of speech recognizer by a significant margin compared to the traditional features.
Besides investigation on HMM theory and speech recognition fundamentals and its implementation in HTK, he has frequently updated his knowledge on recent trends on speech technology.
He has directed interns or freshman on several speech recognition projects for Hindi speech recognition, and he is a consultant of speech recognition in his team. Communication skills are well demonstrated through our collaboration.