Study of deep learning and CMU sphinx in automatic speech recognition

Abhishek Dhankar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Machine learning has proven to be a very effective tool in automatic speech recognition. This paper is an attempt to give a broad overview of the applications of various approaches of machine learning in speech recognition with special reference to deep learning and CMU Sphinx. Deep learning in Speech recognition is a relatively recent development. On the other hand, CMU Sphinx, an open source software has been in use for this purpose for a relatively longer time. CNN, a Deep Learning algorithm learns the invariant features that help it to differentiate between different words and word sequences. CMU Sphinx uses GMM-HMM model to predict the phonemes in the utterance to determine the word or set of continuous words that were spoken.

Original languageEnglish
Title of host publication2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2296-2301
Number of pages6
Volume2017-January
ISBN (Electronic)9781509063673
DOIs
Publication statusPublished - 30-11-2017
Externally publishedYes
Event2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India
Duration: 13-09-201716-09-2017

Conference

Conference2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
CountryIndia
CityManipal, Mangalore
Period13-09-1716-09-17

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Dhankar, A. (2017). Study of deep learning and CMU sphinx in automatic speech recognition. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 2296-2301). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126189