Bernhard Rueber

The thoughtful elephant: strategies for spoken dialog systems

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: to consider available sources of information as early as possible; and to keep alternative hypotheses and delay the decision for a single option as long as possible. We describe how our system architecture caters to incorporating application specific knowledge, including, for example, database constraints, in the determination of the best sentence hypothesis for a user turn. On the next higher level, we use the dialog history to assess the plausibility of a sentence hypothesis by applying consistency checks with information items from previous user turns. In particular, we demonstrate how combination decisions over several turns can be exploited to boost the recognition performance of the system.

Improving speech understanding by incorporating database constraints and dialogue history

In the course of a (man-machine) dialogue, the systems belief concerning the users intention is continuously being built up. Moreover, restricting the discourse to a narrow application domain further constrains the variety of possible user reactions. We show how these knowledge sources may be utilized in a stochastic framework to improve speech understanding. On field test data collected with our automatic exchange board prototype PADIS, a relative reduction of attribute errors by 27% was obtained.

Strategies for name recognition in automatic directory assistance systems

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).

With a little help from the database-developing voice-controlled directory information systems

Automated directory information is amongst the most challenging applications of automatic speech recognition. We present some basic techniques that try to overcome the deficiencies of the speech recognizer by incorporating as much additional knowledge as possible, such as the telephone directory. We derive a maximum a-posteriori decision rule which explicitly uses the telephone directory knowledge as well as the dialogue history to improve speech understanding accuracy. The rule allows us to take a combined decision on the joint probability over multiple dialogue turns, which yields good results in combination with spelling. Our spelling architecture permits continuous spelling of names and uses a context-free grammar to parse common spelling expressions. We review two different real time prototypes, on which we evaluated our decision rule. One (PADIS) operates on a small database and one (PADIS-XL) on a database with 130000 entries.