Recurrent neural network based language model personalization by social network crowdsourcing
Tsung-Hsien Wen, Aaron Heidel, Hung-yi Lee, Yu Tsao, Lin-shan Lee · 2013
Speech recognition has become an important feature in smartphones in recent years. Different from traditional au-tomatic speech recognition, the speech recognition on smart-phones can take advantage of personalized language models to model the linguistic patterns and wording habits of a particu-lar smartphone owner better. Owing to the popularity of social networks in recent years, personal texts and messages are no longer inaccessible. However, data sparseness is still an un-solved problem. In this paper, we propose a three-step adapta-tion approach to personalize recurrent neural network language models (RNNLMs). We believe that its capability to model word histories as distributed representations of arbitrary length can help mitigate the data sparseness problem. Furthermore, we also propose additional user-oriented features to empower the RNNLMs with stronger capabilities for personalization. The experiments on a Facebook dataset showed that the proposed method not only drastically reduced the model perplexity in preliminary experiments, but also moderately reduced the word error rate in n-best rescoring tests.