Gautam chakraborty cognizant technology

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The workshop which was scheduled from 3rd October, exposed students to the screening and analysis of selected portions of canonical and other important, relevant films and whole film, hands-on techno-aesthetic lessons, making presentations, exercises, projects and classroom, lab-based practical exercises with pre-generated customized rushes and self —generated footage including outdoor shooting and making of a documentary proposal, pitch. Speaking on the occasion, Gautam Chakraborty said that the documentaries have never been hotter or more commercially promising—whether you are looking for a career or are out to change the world.

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International Journal of Engineering and Technology, 7 , Nassif, A. Speech recognition using deep neural networks: A systematic review. IEEE Access, 7 , — Padmanabhan, J. Machine learning in automatic speech recognition: A survey. Passricha, V. Journal of Intelligent Systems, 29 1 , — Peddinti, V. A time delay neural network architecture for efficient modelling of long temporal contexts.

Rabiner, L. Fundamentals of speech recognition. PTR Prentice-Hall. Sharma, M. Recurrent neural network based approach to recognize Assamese vowels using experimentally derived acoustic-phonetic features.

In 1st international conference on emerging trends and applications in computer science pp. Learning aided mood and dialect recognition using telephonic speech. Soft computation based spectral and temporal models of linguistically motivated Assamese telephonic conversation recognition.

Shrawankar, U. Techniques for feature extraction in speech recognition system: A comparative study. Sokolov, A. Voice command recognition in intelligent systems using deep neural networks. Sumon, S. Bangla short speech commands recognition using convolutional neural networks. Telmem, M. Waibel, A. Phoneme recognition using time-delay neural networks. Wang, D. Symmetry, 11 , Xie, Y.

Deep learning for natural language processing. In Handbook of statistics Vol. Young, T. Recent trends in deep learning based natural language processing.

Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Gautam Chakraborty. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Reprints and Permissions. Soft-computation based speech recognition system for Sylheti language. Int J Speech Technol 25 , — Download citation. Received : 23 December Accepted : 02 May Published : 25 May Issue Date : June Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content.

Search SpringerLink Search. Abstract The encouraging trend of usage of human machine interfaces in diverse areas has driven the evolution of Automatic Speech Recognition ASR systems during last two decades. References Alotaibi, Y. Article Google Scholar Bhardwaj, I. Google Scholar Cox, D. Article Google Scholar Deka, B.

Google Scholar Deng, L. Article Google Scholar Goldberg, Y. Article Google Scholar Gope, A. Google Scholar Hori, T. Article Google Scholar Kunze, J. Article Google Scholar Nassif, A. Article Google Scholar Padmanabhan, J. Article Google Scholar Passricha, V. Article Google Scholar Peddinti, V. Google Scholar Sharma, M.

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During the workshop, noted filmmaker and teacher, Gautam Chakraborty introduced students to the fundamentals of documentary making, photography, videography and audio recording. He also discussed with students filmmaking techniques and approaches. The workshop which was scheduled from 3rd October, exposed students to the screening and analysis of selected portions of canonical and other important, relevant films and whole film, hands-on techno-aesthetic lessons, making presentations, exercises, projects and classroom, lab-based practical exercises with pre-generated customized rushes and self —generated footage including outdoor shooting and making of a documentary proposal, pitch.

Speaking on the occasion, Gautam Chakraborty said that the documentaries have never been hotter or more commercially promising—whether you are looking for a career or are out to change the world. Follow Us On. Breaking News. Read More. In International conference on image and signal processing , pp. Besacier, L. Automatic speech recognition for under-resourced languages: A survey.

Speech Communication, 56 , 85— Article Google Scholar. Bhardwaj, I. Hidden Markov model based isolated Hindi word recognition. In 2nd International conference on power, control and embedded systems pp. Chakraborty, G. Speech recognition of isolated words using a new speech database in sylheti.

International Journal of Recent Technology and Engineering, 8 , — Google Scholar. Cox, D. Neural networks and neuroscience-inspired computer vision. Current Biology, 24 , R—R Deka, B. Assamese connected digit recognition system. Deng, L. Recent advances in deep learning for speech research at Microsoft.

Dhanashri, D. Isolated word speech recognition system using deep neural networks. In Proceedings of the international conference on data engineering and communication technology pp.

Fausett, L. Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India. Gevaert, W. Neural networks used for speech recognition. Journal of Automatic Control, 20 , 1—7. Goldberg, Y.

Computational Linguistics, 44 , — Gope, A. The phoneme inventory of Sylheti: Acoustic evidences. Journal of Advanced Linguistic Studies, 7 , 7— Hori, T. Minimum word error training of long short-term memory recurrent neural network language models for speech recognition. Kimanuka, U. Turkish speech recognition based on deep neural networks. Kunze, J. Transfer learning for speech recognition on a budget. In Proceedings of the 2nd workshop on representation learning for NLP pp.

Association for Computational Linguistics. Nagajyothi, D. Speech recognition using convolutional neural networks. International Journal of Engineering and Technology, 7 , Nassif, A. Speech recognition using deep neural networks: A systematic review. IEEE Access, 7 , — Padmanabhan, J. Machine learning in automatic speech recognition: A survey.

Passricha, V. Journal of Intelligent Systems, 29 1 , — Peddinti, V. A time delay neural network architecture for efficient modelling of long temporal contexts. Rabiner, L. Fundamentals of speech recognition. PTR Prentice-Hall. Sharma, M. Recurrent neural network based approach to recognize Assamese vowels using experimentally derived acoustic-phonetic features. In 1st international conference on emerging trends and applications in computer science pp. Learning aided mood and dialect recognition using telephonic speech.

Soft computation based spectral and temporal models of linguistically motivated Assamese telephonic conversation recognition.

Shrawankar, U. Techniques for feature extraction in speech recognition system: A comparative study. Sokolov, A. Voice command recognition in intelligent systems using deep neural networks.

Sumon, S.