Abstract Automatic dialog systems have become a mainstream part of online customer service. Many such systems are built, maintained, and improved by customer service specialists, rather than dialog systems engineers and computer programmers. As conversations between people and machines become commonplace, it is critical to understand what is working, what is not, and what actions can be taken to reduce the frequency of inappropriate system responses. These analyses and recommendations need to be presented in terms that directly reflect the user experience rather than the internal dialog processing. This paper introduces and explains the use of Actionable Conversational Quality Indicators (ACQIs), which are used both to recognize parts of dialogs that can be improved and to recommend how to improve them. This combines benefits of previous approaches, some of which have focused on producing dialog quality scoring while others have sought to categorize the types of errors the dialog system is making. We demonstrate the effectiveness of using ACQIs on LivePerson internal dialog systems used in commercial customer service applications and on the publicly available LEGOv2 conversational dataset. We report on the annotation and analysis of conversational datasets showing which ACQIs are important to fix in various situations. The annotated datasets are then used to build a predictive model which uses a turn-based vector embedding of the message texts and achieves a 79% weighted average f1-measure at the task of finding the correct ACQI for a given conversation. We predict that if such a model worked perfectly, the range of potential improvement actions a bot-builder must consider at each turn could be reduced by an average of 81%.

IEEE 1500 core wrappers supporting a hybrid scan mode provide for lower test times with minimal wiring and logic overheads. Wrapper logic and vector formats that are easily integrated with modern IC/FPGA design flows are demonstrated.

In this study, a compact but effective Internet-based support system was designed for Japanese English learners to improve their English pronunciation. The system provides several interactive methods for users not only to learn pronunciation from the sample data of native speakers, but also to discover and evaluate their own specific pronunciation problems, and then improve their pronunciation with the help of the system. The system is real-time, Internet based, and the pinpoint feedback from the system after it has analyzed the user's input sounds, will appear in human teacher-like natural language, rather than impersonal scores or abstract bar graphs which appear in other systems. Our present system focuses on pronunciations that are often problematic for Japanese learners of English. During the process of designing the system, we embedded HMM/DMM speech recognition and speech synthesis modules.

Several industrial and academic research groups are working to simplify the control of appliances and services by creating a truly universal remote control. Unlike the preprogrammed remote controls available today, these new controllers download a specification from the appliance or service and use it to automatically generate a remote control interface. This promises to be a useful approach because the specification can be made detailed enough to generate both speech and graphical interfaces. Unfortunately, generating good user interfaces can be difficult. Based on user studies and prototype implementations, this paper presents a set of requirements that we have found are needed for automatic interface generation systems to create high-quality user interfaces.

The personal universal controller (PUC) is an approach for improving the interfaces to complex appliances by introducing an intermediary graphical or speech interface. A PUC engages in two-way communication with everyday appliances, first downloading a specification of the appliance's functions, and then automatically creating an interface for controlling that appliance. The specification of each appliance includes a high-level description of every function, a hierarchical grouping of those functions, and dependency information, which relates the availability of each function to the appliance's state. Dependency information makes it easier for designers to create specifications and helps the automatic interface generators produce a higher quality result. We describe the architecture that supports the PUC, and the interface generators that use our specification language to build high-quality graphical and speech interfaces.