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A sub-optimal viterbi-like search for linear dynamic models classification

Oikonomidis Dimitrios, Diakoloukas Vasilis, Digalakis Vasilis

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URIhttp://purl.tuc.gr/dl/dias/0DD690B7-99CD-482F-92F7-8362921416EF-
Identifierhttp://www.telecom.tuc.gr/~vas/papers/SearchLDS.pdf-
Languageen-
Extent4 pagesen
TitleA sub-optimal viterbi-like search for linear dynamic models classificationen
CreatorOikonomidis Dimitriosen
CreatorΟικονομιδης Δημητριοςel
CreatorDiakoloukas Vasilisen
CreatorΔιακολουκας Βασιλeioςel
CreatorDigalakis Vasilisen
CreatorΔιγαλακης Βασιληςel
Content SummaryThis paper describes a Viterbi-like decoding algorithm applied on segment-models based on linear dynamic systems (LDMs). LDMs are a promising acoustic modeling scheme which can alleviate several of the limitations of the popular Hidden Markov Models (HMMs). There are several implementations of LDMs that can be found in the literature. For our decoding experiments we consider general identifiable forms of LDMs which allow increased state space dimensionality and relax most of the constraints found in other approaches. Results on the AURORA2 database show that our decoding scheme significantly outperforms standard HMMs, particularly under significant noise levels.en
Type of ItemΔημοσίευση σε Συνέδριοel
Type of ItemConference Publicationen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-08-
Date of Publication2007-
SubjectSpeech recognitionen
Subjectspeech perceptionen
Subjectspeech recognitionen
Bibliographic CitationD. Oikonomidis, V. Diakoloukas and V. Digalakis, "A sub-optimal viterbi-like search for linear dynamic models classification," presented at 8th Annual Conference of the International Speech Communication Association, Antwerp, Belgium, 2007 en

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