@inproceedings{zweig2011personalizing,
author = {Zweig, Geoffrey and Chang, Shawn},
title = {Personalizing Model M for Voice-search},
booktitle = {Interspeech},
year = {2011},
month = {January},
abstract = {Model Mis a recently proposed class based exponential n-gram language model. In this paper, we extend it with personalization features, address the scalability issues present with large data sets, and test its effectiveness on the Bing Mobile voice-search task. We find that Model M by itself reduces both perplexity and word error rate compared with a conventional model, and that the personalization features produce a further significant improvement. The personalization features provide a very large improvement when the history contains a relevant query; thus the overall effect is gated by the number of times a user requeries a past request.},
publisher = {International Speech Communication Association},
url = {http://approjects.co.za/?big=en-us/research/publication/personalizing-model-m-for-voice-search/},
edition = {Interspeech},
}