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Volumn , Issue , 2010, Pages 1341-1344

Boosting systems for LVCSR

Author keywords

Boosting; Speech recognition

Indexed keywords

ADAPTIVE BOOSTING; AGGREGATES; DECISION TREES; ITERATIVE DECODING; SPEECH COMMUNICATION; ITERATIVE METHODS; SPEECH RECOGNITION;

EID: 79959815831     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (4)

References (12)
  • 1
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Y. Freund and R. Shapire, "A decision-theoretic generalization of on-line learning and an application to boosting," in Journal of Computer and System Sciences, 1997.
    • (1997) Journal of Computer and System Sciences
    • Freund, Y.1    Shapire, R.2
  • 2
    • 0033693373 scopus 로고    scopus 로고
    • Boosting Gaussian mixtures in an LVCSR system
    • G. Zweig and M. Padmanabhan, "Boosting Gaussian mixtures in an LVCSR system," in ICASSP-00, 2000.
    • (2000) ICASSP-00
    • Zweig, G.1    Padmanabhan, M.2
  • 3
    • 4544236424 scopus 로고    scopus 로고
    • Boosting HMMs with an application to speech recognition
    • C. Dimitrakakis and S. Bengio, "Boosting HMMs with an application to speech recognition," in ICASSP-04, 2004.
    • (2004) ICASSP-04
    • Dimitrakakis, C.1    Bengio, S.2
  • 4
    • 79959842605 scopus 로고    scopus 로고
    • A frame level boosting training scheme for acoustic modeling
    • R. Zhang and A. Rudnicky, "A frame level boosting training scheme for acoustic modeling," in Interspeech-04, 2004.
    • (2004) Interspeech-04
    • Zhang, R.1    Rudnicky, A.2
  • 5
    • 78049386242 scopus 로고    scopus 로고
    • Toward robust learning of the Gaussian mixture state emission densities for hidden Markov models
    • H. Tang, M. Hasegawa-Johnson, and T. Huang, "Toward robust learning of the Gaussian mixture state emission densities for hidden Markov models," in ICASSP-10, 2010.
    • (2010) ICASSP-10
    • Tang, H.1    Hasegawa-Johnson, M.2    Huang, T.3
  • 6
    • 33646818291 scopus 로고    scopus 로고
    • Constructing ensembles of ASR systems using randomized decision trees
    • O. Siohan, B. Ramabhadran, and B. Kingsbury, "Constructing ensembles of ASR systems using randomized decision trees," in ICASSP-05, 2005.
    • (2005) ICASSP-05
    • Siohan, O.1    Ramabhadran, B.2    Kingsbury, B.3
  • 7
    • 79959833868 scopus 로고    scopus 로고
    • Building multiple complementary systems using directed decision trees
    • C. Breslin and M. Gales, "Building multiple complementary systems using directed decision trees," in Interspeech-07, 2007.
    • (2007) Interspeech-07
    • Breslin, C.1    Gales, M.2
  • 8
    • 33947247727 scopus 로고    scopus 로고
    • How to make adaboost.M1 work for weak base classifiers by changing only one line of the code
    • G. Eibl and K.P. Pfeiffer, "How to make Adaboost.M1 work for weak base classifiers by changing only one line of the code," in ECML-02, 2002.
    • (2002) ECML-02
    • Eibl, G.1    Pfeiffer, K.P.2
  • 9
    • 79959858659 scopus 로고    scopus 로고
    • The IBM attila speech recognition toolkit
    • Submitted
    • H. Soltau, G. Saon, and B. Kingsbury, "The IBM Attila speech recognition toolkit," in Interspeech-10, 2010. Submitted.
    • (2010) Interspeech-10
    • Soltau, H.1    Saon, G.2    Kingsbury, B.3
  • 11
    • 84867211272 scopus 로고    scopus 로고
    • Penalty function maximization for large margin HMM training
    • G. Saon and D. Povey, "Penalty function maximization for large margin HMM training," in Interspeech-08, 2008.
    • (2008) Interspeech-08
    • Saon, G.1    Povey, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.