메뉴 건너뛰기




Volumn 21, Issue 3, 2007, Pages 423-442

Ginisupport vector machines for segmental minimum Bayes risk decoding of continuous speech

Author keywords

[No Author keywords available]

Indexed keywords

HIDDEN MARKOV MODELS; MATHEMATICAL MODELS; REGRESSION ANALYSIS; SPEECH CODING; SPEECH RECOGNITION;

EID: 33847611644     PISSN: 08852308     EISSN: 10958363     Source Type: Journal    
DOI: 10.1016/j.csl.2006.08.002     Document Type: Article
Times cited : (7)

References (46)
  • 1
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • Blum A., and Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 97 1-2 (1997) 245-271
    • (1997) Artificial Intelligence , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.1    Langley, P.2
  • 2
    • 0026966646 scopus 로고    scopus 로고
    • Boser, B., Guyon, I., Vapnik, V., 1992. A training algorithm for optimal margin classifier. In: Proceedings of the 16th Conference on Computational Learning Theory, pp. 144-152.
  • 3
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 121-167
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 4
    • 33645766076 scopus 로고    scopus 로고
    • Byrne, W., 2006. Minimum Bayes risk estimation and decoding in large vocabulary continuous speech recognition. In: Proceedings of the Institute of Electronics, Information, and Communication Engineers, Japan - Special Section on Statistical Modeling for Speech Processing E89-D (3), March.
  • 5
    • 33847628030 scopus 로고    scopus 로고
    • Chakrabartty, S., 2003. The giniSVM toolkit, Version 1.2. Available from: .
  • 6
    • 33847613380 scopus 로고    scopus 로고
    • Chakrabartty, S., 2004. Design and implementation of ultra-low power pattern and sequence decoders. Ph.D. Thesis, The Johns Hopkins University.
  • 7
    • 84958744857 scopus 로고    scopus 로고
    • Forward decoding kernel machines: a hybrid HMM/SVM approach to sequence recognition
    • Proceedings of the SVM'2002, MIT Press, Cambridge
    • Chakrabartty S., and Cauwenberghs G. Forward decoding kernel machines: a hybrid HMM/SVM approach to sequence recognition. Proceedings of the SVM'2002. Lecture Notes in Computer Science vol. 2388 (2002), MIT Press, Cambridge 278-292
    • (2002) Lecture Notes in Computer Science , vol.2388 , pp. 278-292
    • Chakrabartty, S.1    Cauwenberghs, G.2
  • 8
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes C., and Vapnik V. Support-vector networks. Machine Learning 20 3 (1995) 273-297
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 9
    • 33847633971 scopus 로고    scopus 로고
    • Doumpiotis, V., 2005. Discriminative training for speaker adaptation and minimum Bayes risk estimation in large vocabulary speech recognition. Ph.D. Thesis, The Johns Hopkins University.
  • 10
    • 0141814717 scopus 로고    scopus 로고
    • Doumpiotis, V., Tsakalidis, S., Byrne, W., 2003a. Discriminative training for segmental minimum Bayes risk decoding. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, pp. 136-139.
  • 12
    • 0034849711 scopus 로고    scopus 로고
    • Fine, S., Navrátil, J., Gopinath, R., 2001. A hybrid GMM/SVM approach to speaker identification. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, UT, USA, pp. 417-420.
  • 13
    • 0030638031 scopus 로고    scopus 로고
    • Fiscus, J., 1997. A post-processing system to yield reduced word error rates: recognizer output voting error reduction (ROVER). EEE Workshop on Speech Recognition & Understanding, pp. 347-354.
  • 14
    • 85009133711 scopus 로고    scopus 로고
    • Ganapathiraju, A., Hamaker, J., Picone, J., 2000. Hybrid svm/hmm architectures for speech recognition. In: Proceedings of the International Conference on Spoken Language Processing, vol. 4, Beijing, China, pp. 504-507.
  • 15
    • 0001492251 scopus 로고    scopus 로고
    • Minimum Bayes-risk automatic speech recognition
    • Goel V., and Byrne W. Minimum Bayes-risk automatic speech recognition. Computer Speech and Language 14 2 (2000) 115-135
    • (2000) Computer Speech and Language , vol.14 , Issue.2 , pp. 115-135
    • Goel, V.1    Byrne, W.2
  • 16
    • 85009178203 scopus 로고    scopus 로고
    • Minimum Bayes-risk automatic speech recognition
    • Chou W., and Juang B.-H. (Eds), CRC Press Chapter 3
    • Goel V., and Byrne W. Minimum Bayes-risk automatic speech recognition. In: Chou W., and Juang B.-H. (Eds). Pattern Recognition in Speech and Language Processing (2003), CRC Press 51-80 Chapter 3
    • (2003) Pattern Recognition in Speech and Language Processing , pp. 51-80
    • Goel, V.1    Byrne, W.2
  • 17
    • 84871615157 scopus 로고    scopus 로고
    • Goel, V., Kumar, S., Byrne, W., 2001. Confidence based lattice segmentation and minimum Bayes-risk decoding. In: Proceedings of the European Conference on Speech Communication and Technology, Aalborg, Denmark, vol. 4, pp. 2569-2572.
  • 18
  • 19
    • 33847678153 scopus 로고    scopus 로고
    • Golowich, S.E., Sun, D.X., 1998. A support vector/hidden Markov model approach to phoneme recognition. In: ASA Proceedings of the Statistical Computing Section, pp. 125-130.
  • 20
    • 0036505670 scopus 로고    scopus 로고
    • A comparison of methods for multiclass support vector machines
    • March
    • Hsu C.-W., and Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13 2 (2002) 415-422 March
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.2 , pp. 415-422
    • Hsu, C.-W.1    Lin, C.-J.2
  • 21
    • 84898982939 scopus 로고    scopus 로고
    • Exploiting generative models in discriminative classifiers
    • Kearns M.S., and Cohn D.A. (Eds), MIT Press S.A.S.
    • Jaakkola T., and Haussler D. Exploiting generative models in discriminative classifiers. In: Kearns M.S., and Cohn D.A. (Eds). Advances in Neural Information Processing System (1998), MIT Press 487-493 S.A.S.
    • (1998) Advances in Neural Information Processing System , pp. 487-493
    • Jaakkola, T.1    Haussler, D.2
  • 22
    • 33847684405 scopus 로고    scopus 로고
    • Jelinek, F., 1996. Speech recognition as code-breaking. Tech. Rep. Tech Report No. 5, CLSP, JHU, February.
  • 23
    • 33847609703 scopus 로고    scopus 로고
    • Kumar, S., 2004. Minimum Bayes-risk techniques in automatic speech recognition and statistical machine translation. Ph.D. Thesis, The Johns Hopkins University.
  • 24
    • 85009279391 scopus 로고    scopus 로고
    • Kumar, S., Byrne, W., 2002. Risk based lattice cutting for segmental minimum Bayes-risk decoding. In: Proceedings of the International Conference on Spoken Language Processing, Denver, CO, USA, pp. 373-376.
  • 25
    • 33847651843 scopus 로고    scopus 로고
    • Lafferty, J., McCallum, A., Pereira, F., 2001. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the International Conference on Machine Learning, pp. 282-289.
  • 26
    • 0029288633 scopus 로고
    • Maximum likelihood linear regression for speaker adaptation of continuous density hmms
    • Legetter C.J., and Woodland P.C. Maximum likelihood linear regression for speaker adaptation of continuous density hmms. Computer, Speech and Language 9 (1995) 171-186
    • (1995) Computer, Speech and Language , vol.9 , pp. 171-186
    • Legetter, C.J.1    Woodland, P.C.2
  • 27
    • 0034843104 scopus 로고    scopus 로고
    • Mangu, L., Padmanabhan, M., 2001. Error corrective mechanisms for speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, UT, USA, vol. 1, pp. 29-32.
  • 28
    • 0034296009 scopus 로고    scopus 로고
    • Finding consensus in speech recognition: word error minimization and other applications of confusion networks
    • Mangu L., Brill E., and Stolcke A. Finding consensus in speech recognition: word error minimization and other applications of confusion networks. Computer Speech and Language 14 4 (2000) 373-400
    • (2000) Computer Speech and Language , vol.14 , Issue.4 , pp. 373-400
    • Mangu, L.1    Brill, E.2    Stolcke, A.3
  • 29
    • 33847671910 scopus 로고    scopus 로고
    • Mohri, M., Pereira, F., Riley, M., 2001. AT&T General-purpose Finite-State Machine Software Tools. Available from: .
  • 30
    • 33847642226 scopus 로고    scopus 로고
    • Noel, M., 1997. Alphadigits. CSLU, OGI, Available from: .
  • 31
    • 0028996990 scopus 로고    scopus 로고
    • Normandin, Y., 1995. Optimal splitting of HMM gaussian mixture components with mmie training. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 15, pp. 449-452.
  • 33
    • 85009260832 scopus 로고    scopus 로고
    • Salomon, J., King, S., Osburne, M., 2002. Framewise phone classification using support vector machines. In: Proceedings of the International Conference on Spoken Language Processing, Denver, CO, USA, pp. 2645-2648.
  • 34
    • 33847676871 scopus 로고    scopus 로고
    • Smith, N.D., 2003. Using augmented statistical models and score spaces for classification. Ph.D. Thesis, Christ's College.
  • 35
    • 84898996216 scopus 로고    scopus 로고
    • Smith, N., Gales, M., 2002a. Speech recognition using svms. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1197-1204.
  • 36
    • 33847645927 scopus 로고    scopus 로고
    • Smith, N.D., Gales, M.J.F., April 2002b. Using SVMs to classify variable length speech patterns. Tech. Rep. CUED/F-INFENG/TR412, Cambridge University, Engineering Department.
  • 37
    • 85009080427 scopus 로고    scopus 로고
    • Smith, N., Niranjan, M., 2000. Data-dependent kernels in svm classification of speech patterns. In: Proceedings of the International Conference on Spoken Language Processing, Beijing, China, pp. 297-300.
  • 38
    • 33847623059 scopus 로고    scopus 로고
    • Smith, N.D., Gales, M.J.F., Niranjan, M., April 2001. Data-dependent kernels in SVM classification of speech patterns. Tech. Rep. CUED/F-INFENG/TR387, Cambridge University, Engineering Department.
  • 40
    • 33847641064 scopus 로고    scopus 로고
    • Venkataramani, V., 2005. Code breaking for automatic speech recognition. Ph.D. Thesis, The Johns Hopkins University.
  • 41
    • 33645775754 scopus 로고    scopus 로고
    • Venkataramani, V., Byrne, W., 2003. Support vector machines for segmental minimum Bayes risk decoding of continuous speech. IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 13-18.
  • 42
    • 33645759970 scopus 로고    scopus 로고
    • Venkataramani, V., Byrne, W., 2005. Lattice segmentation and support vector machines for large vocabulary continuous speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 817-820.
  • 43
    • 0031630644 scopus 로고    scopus 로고
    • Wessel, F., Macherey, K., Schlueter, R., 1998. Using word probabilities as confidence measures. In: Proceedings of the ICASSP, Seattle, WA, USA, pp. 225-228.
  • 44
    • 33847667578 scopus 로고    scopus 로고
    • Weston, J., Watkins, C., May 1999. Support vector machines for multi-class pattern recognition. In: Proceedings of the 7th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 219-224.
  • 45
    • 33847645472 scopus 로고    scopus 로고
    • Woodland, P.C., Povey, D., 2000. Large scale discriminative training for speech recognition. In: Proceedings of the ITW ASR, ISCA, pp. 7-16.
  • 46
    • 33847641499 scopus 로고    scopus 로고
    • Young, S., Evermann, G., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P., July 2000. The HTK Book, Version 3.0.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.