Paper: Multi-scale Personalization for Voice Search Applications

ACL ID N09-2026
Title Multi-scale Personalization for Voice Search Applications
Venue Human Language Technologies
Session Short Paper
Year 2009

Voice Search applications provide a very con- venient 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 er- rors. This paper explores the utilization of per- sonalization 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 reduct...