A Cache-Based Natural Language Model for Speech Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 12, Issue 6, 1990, Pages 570-583

  Kuhn, Roland ^a   Mori, Renato De ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

3g gram language model; Cache based language model; language models for speech recognition; Markov language models; natural language; perplexity; probabilistic language model

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS;

MARKOV LANGUAGE MODELS; PROBABILISTIC LANGUAGE MODELS;

SPEECH;

EID: 0025446887     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.56193     Document Type: Article

References (13)

1. A. M. Derouault and B. Merialdo, “Natural language modeling for phoneme-to-text transcription,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, pp. 742–749, Nov. 1986.
2. —, “Language modeling at the syntactic level,” in Proc. 7th Int. Conf. Pattern Recognition, vol. II, Montreal, Aug. 1984, pp. 1373–1375.
3. F. Jelinek, “The development of an experimental discrete dictation recognizer,” Proc. IEEE, vol. 73, no. 11, pp. 1616–1624, Nov. 1985.
4. F. Jelinek, R. L. Mercer, and L. R. Bahl, “A maximum likehood approach to continuous speech recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, pp. 179–90, Mar. 1983.
5. F. Jelinek and R. L. Mercer, “Interpolated estimation of Markov source parameters from sparse data,” in Pattern Recognition in Practice, E. S. Gelsema and L. H. Kanal, Eds. 1981, pp. 381–397.
6. S. Johansson, E. Atwell, R. Garside, and G. Leech, The Tagged LOB Corpus User'S Manual, Norwegian Computing Centre for the Humanities, Bergen, Norway, 1986.
7. S. Johansson, “Some observations on word frequencies in three corpora of present-day English texts,” ITL Rev. Appl. Linguistics, vol. 67–68, pp. 117–126, 1985.
8. —, “Word frequency and text type: Some observations based on the LOB corpus of British English texts,” Comput. Humanities, vol. 19, pp. 23–36, 1985.
9. S. Katz. “Recursive M-gram modeling via a smoothing of Turing'S formula,” forthcoming paper.
10. E. M. Muckstein, “A natural language parser with statistical applications,” IBM Res. Rep. RC7516 (38450), Mar. 1981.
11. A. Nadas, “Estimation of probabilities in the language model of the IBM speech recognition system,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 32, pp. 859–861, Aug. 1984.
12. J. Peterson and A. Silberschatz, Operating System Concepts. Reading, MA: Addison-Wesley, 1983.
13. L. R. Rabiner and B. H. Juang, “An introduction to hidden Markov models,” IEEE ASSP Mag., pp. 4–16, Jan. 1986.