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In this paper we present technology used in spoken dialog systems for applications of a wide range. They include tasks from the travel domain and automatic switchboards as well as large scale directory assistance. The overall goal in developing spoken dialog systems is to allow for a natural and flexible dialog flow similar to human--human interaction. This imposes the challenging task to recognize and interpret user input, where he/she is allowed to choose from an unrestricted vocabulary and an infinite set of possible formulations. We therefore put emphasis on strategies that make the system more robust while still maintaining a high level of naturalness and flexibility. In view of this paradigm, we found that two fundamental principles characterize many of the proposed methods: 1) to consider available sources of information as early as possible, and 2) to keep alternative hypotheses and delay the decision for a single option as long as possible. We describe

Abstract The commercial viability of automating large scale directory assistance is shown by presenting new results on the recognition of large numbers of different names. Satisfactory recognition performance is achieved by employing a stochastic combination of N-best lists retrieved from multiple user utterances with the telephone database as an additional knowledge source. The 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 for first and last name of the desired person and, if necessary, also for the street or a spelling of the last name. Confidence measures are used for an optimal dialogue flow.

The automated directory assistance system (ADAS) is traditionally formulated as an automatic speech recognition (ASR) problem. Recently, it has been formulated as a voice search problem, where a spoken utterance is firstly converted into text, which in turn is used to search for the listing. In this paper, we focus on the design and development of the utterance-to-listing component of ADAS. We show that many theoretical and practical issues need to be resolved when applying the basic idea of voice search to the development of ADAS. We share our experiences in addressing these issues, especially in pre-processing the listing database, generating a high performance LM, and developing efficient, accurate, and robust search algorithms. Field tests of our prototype system indicate that an 81 % task completion rate can be achieved.

This paper focuses on the challenges that one encounters when building for commercial deployment an automated system for Directory Assistance (DA.) The design for an automated DA system needs to take into account constraints and requirements that arise from three distinct aspects of the application, namely, the business drivers, the user needs, and the strengths and weaknesses of voice technologies. 1.

this paper we propose a portable Auto Attendant System (AAS) with sophisticated dialog structure that gives a caller more flexibility while calling. The caller may interact with the system to request a phone number by providing just a work area, specialty, surname, or title, etc. If the party is absent, the system may provide extra information such as where he went, when he will be back, and what he is doing. The system is built modularly, with components such as speech recognizer, language model, dialog manager and text-to-speech that can be replaced if necessary. By simply changing the personnel record database, the system can easily be ported to other companies. The sophisticated dialog manager applies many strategies to allow natural interaction between user and system. Functions such as fuzzy request, user repairing, and extra information query, which are not provided by other systems, are integrated into our system

Directory assistance systems are amongst the most challenging applications of speech recognition. Today, complete automation of the service fails because of the lacking accuracy of current speech recognizers, which are simply not able to differentiate between hundreds of thousands or even millions of different names occurring in large cities. In this paper, we show that this situation can be remedied by systematically combining all available knowledge sources (last names, first names, street names, partly including their spelled versions) in a statistically optimal way. Especially designed confidence measures for N-best lists are proposed to detect misrecognized turns.

Voice search is increasingly popular, especially for local business directory assistance. However, speech recognition accuracy on business listing names is still low, leading to user frustration. In this paper, we present a new algorithm for geo-centric language model generation for local business voice search for mobile users. Our algorithm has several advantages: it provides a language model for any user in any location; the geographic area covered by the language model is adapted to the local business density, giving high recognition accuracy; and the language models can be pre-compiled, giving fast recognition time. In an experiment using spoken business listing name queries from a business directory assistance service, we achieve a 16.8 % absolute improvement in recognition accuracy and a 3-fold speedup in recognition time with geocentric language models when compared with a nationwide language model. 1