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Estimation of probabilities from sparse data for the language model component of a speech recognizer
Abstract
The description of a novel type of m-gram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. While the method has been developed for and successfully implemented in the IBM Real Time Speech Recognizers, its generality makes it applicable in other areas where the problem of estimating probabilities from sparse data arises.
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Cited by
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Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval
Zhai, C., & Lafferty, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130377 · Cited by 1,537
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., & Robinson, T. (2013) · arXiv · doi:10.48550/arxiv.1312.3005 · Cited by 1,051
Topic Detection and Tracking Pilot Study Final Report
Allan, J., Carbonell, J., Doddington, G. R., Yamron, J., & Yang, Y. (2018) · doi:10.1184/r1/6626252.v1 · Cited by 939
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Metadata
- Authors
- Katz, S.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1987-03
- Volume
- 35
- Issue
- 3
- Pages
- 400-401
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2134237567
- Cited by
- 1,637
- References
- 0
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Katz, S. (1987). Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/tassp.1987.1165125
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