Natural language call steering for service applications

Abstract

ABSTRACT In this paper, a dialogue system for natural language based call steering is described and studied. The system is based on natural language speech recognition and understanding within a mixed initiative dialogue. The system is implemented on Bell Labs. Speech Technology Integration Platform (BLSTIP) using dialogue and natural language understanding components from BT laboratories. A prototype system in the operator service domain [2] is described. In order to improve the acoustic and language modeling for natural language based dialogue applications, various approaches are described and studied. The structure of the dialogue manager is also presented in which mixed-initiative dialogue can be supported with efficiency. Call classification and steering experiments were performed. The results confirm the efficacy of the proposed approach. 1. INTRODUCTION Natural language dialogue between human and machine is a challenge. In order to make a natural language based dialogue system successful, various efforts are made to improve the accuracy, flexibility and robustness of the system component technologies, such as speech recognition, speech understanding, dialogue generation and dialogue manager, text-to-speech synthesis, etc. Such a complex dialogue application imposes stringent requirements on the flexibility of the system platform. One of the drawbacks in systems deployed in the past is the limitation imposed by the finite state grammar on the language that a user can use to communicate with the machine. Although such constraint alleviates the complexity and problem in recognizing human speech, it becomes an obstacle to support more powerful, user friendly and flexible dialogue systems for mixed-initiative dialogues. In this paper, we study issues encountered in designing and implementing a natural language based call steering application for telephone service calls. This is a complicated application, and it performs a detailed diagnostic dialogue to identify the service problem, such as a troubled telephone line and etc., that the user is experiencing. It provides the desired service after receiving user’s consent and confirmation [2]. In the prototype system studied in this paper, the dialogue can go deep through many turns. The natural language based request and query from the user is recognized through natural language based automatic speech recognition. There is no constraint on the way that the user should communicate to the system. It allows the user to make direct requests as well as provide a description of the problem where the final action will be identified as the outcome of the dialogue. A call classifier provides natural language understanding based on the word string from the speech recognition output. The dialogue manager uses this understanding to determine the next appropriate system action. The organization of this paper is as follows. In Section 2, the dialogue system architecture and design are presented which support natural language based mixed-initiative dialogue applications such as call steering, movie locator, etc. Section 3 is devoted to natural language based speech recognition and statistical language modeling for dialogue applications. Section 4 is concentrated on the dialogue manager design and automatic query generation. Call classification and steering are studied in Section 5 and results are given based on a case study in a telephone service application.