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  1. KEEP
  2. Theses and Dissertations
  3. Barrett, The Honors College Thesis/Creative Project Collection
  4. A Guide to Speech Recognition Algorithms
  5. Full metadata

A Guide to Speech Recognition Algorithms

Full metadata

Title
A Guide to Speech Recognition Algorithms
Description
Many tasks that humans do from day to day are taken for granted in term of appreciating their true complexity. Humans are the only species on the planet that have developed such an in-depth means of auditory communication. Recreating the mechanisms in the brain that recognize speech patterns is no easy task. This paper compares and contrasts various algorithms used in modern day ASR systems, and focuses primarily on ASR systems in resource constrained environments. The Green colored blocks in Figure 1 will be focused on in greater detail throughout this paper, they are the key to building an exceptional ASR system. Deep Neural Networks (DNNs) are the clear and current leader among ASR technologies; all research in this field is currently revolving around this method. Although DNNs are very effective, many older methods of ASR are used often due to the complexities involved with DNNs; these difficulties include the large amount of hardware resources as well as development resources, such as engineers and money, required for this method.
Date Created
2015-12
Contributors
  • Petersen, Casey Alexander (Author)
  • Csavina, Kristine (Thesis director)
  • Pollat, Scott (Committee member)
  • Engineering Programs (Contributor)
  • Barrett, The Honors College (Contributor)
Topical Subject
  • Speech Recognition
  • HMM
  • MFCC
  • LPC
Resource Type
Text
Extent
13 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Barrett, The Honors College Thesis/Creative Project Collection
Series
Academic Year 2015-2016
Handle
https://hdl.handle.net/2286/R.I.36111
Level of coding
minimal
Cataloging Standards
asu1
System Created
  • 2017-10-30 02:50:57
System Modified
  • 2021-08-11 04:09:57
  •     
  • 2 years 3 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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