Personalizing Recurrent-Neural-Network-Based Language Model by Social Network

With the popularity of mobile devices, personalized speech recognizers have become more attainable and are highly attractive. Since each mobile device is used primarily by a single user, it is possible to have a personalized recognizer that well matches the characteristics of the individual user. Although acoustic model personalization has been investigated for decades, much less work has been reported on personalizing language models, presumably because of the difficulties in collecting sufficient personalized corpora. In this paper, we propose a general framework for personalizing recurrent-neural-network-based language models (RNNLMs) using data collected from social networks, including the posts of many individual users and friend relationships among the users. Two major directions for this are model-based and feature-based RNNLM personalization. In model-based RNNLM personalization, the RNNLM parameters are fine-tuned to an individual user's wording patterns by incorporating social texts posted by the target user and his or her friends. For the feature-based approach, the RNNLM model parameters are fixed across users, but the RNNLM input features are instead augmented with personalized information. Both approaches not only drastically reduce the model perplexity, but also moderately reduce word error rates in n-best rescoring tests.