Adaptive language modeling using minimum discriminant estimation

• Published in: [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 1; pp. 633 - 636 vol.1
• Main Authors: Della Pietra, S., Della Pietra, V., Mercer, R.L., Roukos, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1992
• Subjects: Distortion measurement; Entropy; Fires; Frequency estimation; Insurance; Natural languages; Predictive models; Probability; Speech recognition; Vocabulary
• ISBN: 9780780305328; 0780305329
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.1992.225829

The authors present an algorithm to adapt a n-gram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that already occurred. Then, they find the closest n-gram distribution to the static n-gram distribution (using the discrimination information distance measure) that satisfies the marginal constraints derived from the document. The resulting minimum discrimination information model results in a perplexity of 208 instead of 290 for the static trigram model on a document of 321 words.< >