Semiautomatic Improvements of System-Initiative Spoken Dialog Applications Using Interactive Clustering

Abstract—While many successful spoken dialog systems have been deployed over telephone networks in recent years, the high cost of developing such applications has led to limited adoption. Despite large research efforts in user-initiative and mixed-initiative systems, most commercial applications follow a system initiative approach because they are simpler to design and are found to work adequately. Yet, even designing such system-initiative spoken dialog systems has proven costly when compared with simpler touchtone systems. To address this issue, we describe in this paper our efforts in building diagnostics tools to let nonexperienced speech developers write usable applications without the need for transcribing calls. Our approach consists of two steps. In the first step, we cluster calls based on Question/Answer (QA) states and transitions, analyze the success rates associated with each QA state and transition, and identify the most problematic QA states and transitions based on a criterion we call Arc Cut Gain in Success Rate (ACGSR). In the second step, we cluster calls associated with problematic QA transitions through an approach we term Interactive Clustering (IC). The purpose of this step is to automatically cluster calls that are similar to those already labeled by the developers to maximize productivity. Experiments on an internal auto-attendant application show that our approach can significantly reduce the time and effort needed to identify problems in spoken dialog applications.

Index Terms—Automatic analysis, call transition diagram, data mining, model-based clustering, semi-supervised clustering, speech recognition.

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In  IEEE Trans. Speech & Audio Proc (Special Issue on Data Mining of Speech, Audio and Dialog)

Publisher  IEEE
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