An articulatorily constrained, maximum entropy approach to speech recognition and speech coding

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Hidden Markov models (HMM`s) are among the most popular tools for performing computer speech recognition. One of the primary reasons that HMM`s typically outperform other speech recognition techniques is that the parameters used for recognition are determined by the data, not by preconceived notions of what the parameters should be. This makes HMM`s better able to deal with intra- and inter-speaker variability despite the limited knowledge of how speech signals vary and despite the often limited ability to correctly formulate rules describing variability and invariance in speech. In fact, it is often the case that when HMM parameter values are ... continued below

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10 p.

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Hogden, J. December 31, 1996.

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Description

Hidden Markov models (HMM`s) are among the most popular tools for performing computer speech recognition. One of the primary reasons that HMM`s typically outperform other speech recognition techniques is that the parameters used for recognition are determined by the data, not by preconceived notions of what the parameters should be. This makes HMM`s better able to deal with intra- and inter-speaker variability despite the limited knowledge of how speech signals vary and despite the often limited ability to correctly formulate rules describing variability and invariance in speech. In fact, it is often the case that when HMM parameter values are constrained using the limited knowledge of speech, recognition performance decreases. However, the structure of an HMM has little in common with the mechanisms underlying speech production. Here, the author argues that by using probabilistic models that more accurately embody the process of speech production, he can create models that have all the advantages of HMM`s, but that should more accurately capture the statistical properties of real speech samples--presumably leading to more accurate speech recognition. The model he will discuss uses the fact that speech articulators move smoothly and continuously. Before discussing how to use articulatory constraints, he will give a brief description of HMM`s. This will allow him to highlight the similarities and differences between HMM`s and the proposed technique.

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10 p.

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OSTI as DE97002784

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  • Other Information: PBD: [1996]

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  • Other: DE97002784
  • Report No.: LA-UR--96-3519
  • Grant Number: W-7405-ENG-36
  • DOI: 10.2172/432946 | External Link
  • Office of Scientific & Technical Information Report Number: 432946
  • Archival Resource Key: ark:/67531/metadc685984

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Creation Date

  • December 31, 1996

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • May 20, 2016, 1:02 p.m.

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Hogden, J. An articulatorily constrained, maximum entropy approach to speech recognition and speech coding, report, December 31, 1996; New Mexico. (digital.library.unt.edu/ark:/67531/metadc685984/: accessed December 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.