Investigations on convex optimization using log-linear HMMs for digit string recognition

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2009, Pages 216-219

  Heigold, Georg ^a   Rybach, David ^a   Schlüter, Ralf ^a   Ney, Hermann ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Acoustic modeling; Conditional random fields; Convex optimization; Digit string recognition

Indexed keywords

ACOUSTIC MODEL; ACOUSTIC MODELING; BASELINE MODELS; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS; CONVEX OPTIMIZATION TECHNIQUES; DISCRIMINATIVE METHODS; DISCRIMINATIVE TRAINING; LOGLINEAR MODEL; MODEL PARAMETERS;

ACOUSTIC FIELDS; CELLULAR RADIO SYSTEMS; CONTENT BASED RETRIEVAL; HEURISTIC METHODS; OPTIMIZATION; PARAMETER ESTIMATION; REGRESSION ANALYSIS; SPEECH COMMUNICATION; SPEECH RECOGNITION;

CONVEX OPTIMIZATION;

EID: 70450161334     PISSN: None     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: probabilistic models for segmenting and labeling sequence data," in ICML, San Francisco, CA, 2001.
2. H.-K. J. Kuo and Y. Gao, "Maximum entropy direct models for speech recognition," IEEE Trans. on Audio, Speech and Language Processing, vol. 14, no. 3, 2006.
3. Y. Hifny Abdel-Haleem, Conditional random fields for continuous speech recognition, Ph.D. thesis, Faculty of Engineering, University of Sheffield, 2006.
4. A. Gunawardana, M. Mahajan, A. Acero, and J. C. Platt, "Hidden conditional random fields for phone classification," in Interspeech, Lisbon, Portugal, 2005.
5. F. Sha and L.K. Saul, "Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models," in ICASSP, Honolulu, HI, USA, 2007.
6. G. Heigold, P. Lehnen, R. Schlüter, and H. Ney, "On the equivalence of Gaussian and log-linear HMMs," in Interspeech, Brisbane, Australia, 2008.
7. Y. Altun, I. Tsochantaridis, and T. Hofmann, "Hidden Markov support vector machines," in ICML, Washington, DC, USA, 2003.
8. G. Heigold, R. Schlüter, and H. Ney, "Modified MPE/MMI in a transducer-based framework," in ICASSP, Taipei, Taiwan, 2009.
9. D. Kershaw, T. Robinson, and M. Hochberg, "Contextdependent classes in a hybrid recurrent network-HMM speech recognition system," in NIPS, Denver, CO, USA, 1996.
10. M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: The Rprop algorithm," in ICNN, San Francisco, CA, USA, 1993.