Multi-scale Personalization for Voice Search Applications

Voice Search applications provide a very convenient and direct access to a broad variety of services and information. However, due to the vast amount of information available and the open nature of the spoken queries, these applications still suffer from recognition errors. This paper explores the utilization of personalization features for the post-processing of recognition results in the form of n-best lists. Personalization is carried out from three different angles: short-term, long-term and Web-based, and a large variety of features are proposed for use in a log-linear classification framework. Experimental results on data obtained from a commercially deployed Voice Search system show that the combination of the proposed features leads to a substantial sentence error rate reduction. In addition, it is shown that personalization features which are very different in nature can successfully complement each other.