Linked Life Data

21110529

Source:http://linkedlifedata.com/resource/pubmed/id/21110529

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-11-29
pubmed:abstractText
Inspired by recent evidence that a binary pattern may provide sufficient information for human speech recognition, this letter proposes a fundamentally different approach to robust automatic speech recognition. Specifically, recognition is performed by classifying binary masks corresponding to a word utterance. The proposed method is evaluated using a subset of the TIDigits corpus to perform isolated digit recognition. Despite dramatic reduction of speech information encoded in a binary mask, the proposed system performs surprisingly well. The system is compared with a traditional HMM based approach and is shown to perform well under low SNR conditions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1520-8524
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
128
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
EL217-22
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Robust speech recognition from binary masks.
pubmed:affiliation
Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA. narayaar@cse.ohio-state.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.