The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learning string-edit distance
Abstract
In many applications, it is necessary to determine the similarity of two strings. A widely-used notion of string similarity is the edit distance: the minimum number of insertions, deletions, and substitutions required to transform one string into the other. In this report, we provide a stochastic model for string-edit distance. Our stochastic model allows us to learn a string-edit distance function from a corpus of examples. We illustrate the utility of our approach by applying it to the difficult problem of learning the pronunciation of words in conversational speech. In this application, we learn a string-edit distance with nearly one-fifth the error rate of the untrained Levenshtein distance. Our approach is applicable to any string classification problem that may be solved using a similarity function against a database of labeled prototypes.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
SWITCHBOARD: telephone speech corpus for research and development
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134
Cited by
Collective entity resolution in relational data
Bhattacharya, I., & Getoor, L. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217304 · Cited by 593
A Survey on Metric Learning for Feature Vectors and Structured Data
Bellet, A., Habrard, A., & Sebban, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.6709 · Cited by 494
Deep learning for visual speech analysis: A survey
Sheng, C., Kuang, G., Bai, L., Hou, C., Guo, Y., Xu, X., Pietikäinen, M., & Liu, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3376710 · Cited by 64
(2012) · Cited by 43
Deep learning approaches for similarity computation: A survey
Yang, P., Wang, H., Yang, J., Qian, Z., Zhang, Y., & Lin, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3422484 · Cited by 28
Metadata
- Authors
- Ristad, E. S., & Yianilos, P. N.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998-05
- Volume
- 20
- Issue
- 5
- Pages
- 522-532
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2102443632
- Cited by
- 859
- References
- 2
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ristad, E. S., & Yianilos, P. N. (1998). Learning string-edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.682181
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