Investigating linguistic knowledge in a maximum entropy token-based language model

2007 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2007, Proceedings

Volumn , Issue , 2007, Pages 171-176

  Jia, Cui ^a   Yi, Su ^a   Hall, Keith ^a   Jelinek, Frederick ^a  

^a Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; COMPUTER NETWORKS; ERROR ANALYSIS; HIDDEN MARKOV MODELS; LABELING; LABELS; LEARNING SYSTEMS; LINGUISTICS; MARKOV PROCESSES; PROBABILITY DISTRIBUTIONS; SPEECH; SPEECH ANALYSIS; STANDARDS;

AUTOMATIC SPEECH RECOGNITION (ASR); DATA-SETS; DIFFERENT TYPES; HIDDEN MARKOV MODEL (HMM); HIDDEN STATES; LANGUAGE MODELLING; LARGE VOCABULARY SPEECH RECOGNITION; LINGUISTIC INFORMATION; LINGUISTIC KNOWLEDGE; MAXIMUM ENTROPY (MAXENT); MAXIMUM ENTROPY MODEL (MEM); N-GRAM MODELS; OVERLAPPING FEATURES; TRANSITION PROBABILITY (TP); WIDE-RANGE; WORD-ERROR RATE (WRD);

SPEECH RECOGNITION;

EID: 44849116673     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. P. F. Brown, P. V. deSouza, R. L. Mercer, V. J. Della Pietra, and J. C. Lai, "Class-based n-gram models of natural language," Computational Linguistics, vol. 18, no. 4, pp. 467-479, 1992.
2. A. Emami and F. Jelinek, "Random clusterings for language modeling," in Proc. of ICASSP, vol. 1, pp. 581-584.
3. A. L. Berger, S. D. Pietra, and V. J. Della Pietra, "A maximum entropy approach to natural language processing," Computational Linguistics, vol. 22, no. 1, pp. 39-71, 1996.
4. S. Khudanpur and J. Wu, "Maximum entropy techniques for exploiting syntactic,semantic and collocational dependencies in language modeling," Computer Speech and Language, vol. 14, no. 4, 2000.
5. W. Wang and M. P. Harper, "The superarv language model: investigating the effectiveness of tightly integrating multiple knowledge sources," in Proc. of EMNLP, 2002, pp. 238-247.
6. J. Bilmes and K. Kirchhoff, "Factored language models and generalized, parallel backoff," in Proc. of HLT/NACCL, 2003, pp. 4-6.
7. S. Chen and R. Rosenfeld, "A gaussian prior for smoothing maximum entropy models," Tech. Rep. CMUCS -99-108, Carnegie Mellon University, 1999.
8. J. Wu and S. Khudanpur, "Combining nonlocal, syntactic and n-gram dependencies in language modeling," in Proc. of Eurospeech, 1999, pp. 2179-2182.
9. L. E. Baum, T. Petrie, G. Soules, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains," The Annals of Mathematical Statistics, vol. 41, pp. 164-171, 1970.
10. M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz, "Building a large annotated corpus of english: The penn treebank," Computational Linguistics, vol. 19, pp. 313-330, 1993.
11. S. F. Chen and J. T. Goodman, "An empirical study of smoothing techniques for language modeling," in Technical Report TR-10-98, Computer Science Group. 1998, Harvard University.
12. A. Stoicke, "SRILM. - an extensible language modeling toolkit," in Proc. Intl. Conf. on Spoken Language Processing, 2002.
13. D. Lin, "Automatic retrieval and clustering of similar words," in COLING-ACL, 1998, pp. 768-774.
14. D. G. Hays, "Dependency theory: A formalism and some observations," Language, vol. 40, pp. 511-525, 1964.
15. D. Lin and P. Pantel, "Induction of semantic classes from natural language text," in Proc of SIGKDD, 200.1, pp. 317-322.
16. Y. Deng and S. Khudanpur, "Latent semantic information in maximum entropy language models for conversational speech recognition," in HLT-NAACL, May 2003, pp. 56-63.
17. D. Lin, "Proximity-based thesaurus and dependency-based thesaurus," in http://armena.cs.ualberta.ca/lindek/downloads, 2000.
18. H. Soltau, B. Kingsbury, L. Mangu, D. Povey, G. Saon, and G. Zweig, "The IBM 2004 Conversational Telephony System, for Rich Transcription," in Proc of ICASSP, 2005, vol. 1, pp. 205-208.