Personalizing Model M for Voice-search

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.

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In  Interspeech

Publisher  International Speech Communication Association


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