Entry Wu:2011:ADS from talip.bib

Last update: Sun Oct 15 02:55:04 MDT 2017                Valid HTML 3.2!

Index sections

Top | Symbols | Numbers | Math | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

BibTeX entry

@Article{Wu:2011:ADS,
  author =       "Chung-Hsien Wu and Hung-Yu Su and Han-Ping Shen",
  title =        "Articulation-Disordered Speech Recognition Using
                 Speaker-Adaptive Acoustic Models and Personalized
                 Articulation Patterns",
  journal =      j-TALIP,
  volume =       "10",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jun,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/1967293.1967294",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Tue Jun 28 18:29:03 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "This article presents a novel approach to
                 speaker-adaptive recognition of speech from
                 articulation-disordered speakers without a large amount
                 of adaptation data. An unsupervised, incremental
                 adaptation method is adopted for personalized model
                 adaptation based on the recognized syllables with high
                 recognition confidence from an automatic speech
                 recognition (ASR) system. For articulation pattern
                 discovery, the manually transcribed syllables and the
                 corresponding recognized syllables are associated with
                 each other using articulatory features. The Apriori
                 algorithm is applied to discover the articulation
                 patterns in the corpus, which are then used to
                 construct a personalized pronunciation dictionary to
                 improve the recognition accuracy of the ASR. The
                 experimental results indicate that the proposed
                 adaptation method achieves a syllable error rate
                 reduction of 6.1\%, outperforming the conventional
                 adaptation methods that have a syllable error rate
                 reduction of 3.8\%.",
  acknowledgement = ack-nhfb,
  articleno =    "7",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

Related entries