  With the popularity of mobile devices, personalized speech recognizer becomes
more realizable today and highly attractive. Each mobile device is primarily
used by a single user, so it's possible to have a personalized recognizer well
matching to the characteristics of individual user. Although acoustic model
personalization has been investigated for decades, much less work have been
reported on personalizing language model, probably because of the difficulties
in collecting enough personalized corpora. Previous work used the corpora
collected from social networks to solve the problem, but constructing a
personalized model for each user is troublesome. In this paper, we propose a
universal recurrent neural network language model with user characteristic
features, so all users share the same model, except each with different user
characteristic features. These user characteristic features can be obtained by
crowdsouring over social networks, which include huge quantity of texts posted
by users with known friend relationships, who may share some subject topics and
wording patterns. The preliminary experiments on Facebook corpus showed that
this proposed approach not only drastically reduced the model perplexity, but
offered very good improvement in recognition accuracy in n-best rescoring
tests. This approach also mitigated the data sparseness problem for
personalized language models.
