Recognition of large numbers of different names is the central problem in automatic directory assistance services and many other applications for spoken language dialogue systems. This paper investigates a methodology of stochastically combining N-best lists retrieved from multiple user utterances with the telephone database as an additional knowledge source. This strategy is used in a prototype of a fully automated directory information system which is designed to cover a whole country. After the city has been selected, the user is asked to spell and say the name of the desired person and if necessary also the first name and street. The number of active database entries is reduced in every turn until only a single database entry is left. Results for different recognition strategies are presented on a real-life data collection for databases of various sizes with up to 1 million entries (city of Berlin). The experiments show that a substantial part of all simple requests can be automated with the strategy presented (80% correctly recognized, 10% rejected).
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