Current perceptions of Instant Messaging (IM) use are based primarily on self-report studies. We logged thousands of (mostly) workplace IM conversations and evaluated their conversational characteristics and functions. Contrary to prior research, we found that the primary use of workplace IM was for complex work discussions. Only 28% of conversations were simple, single-purpose interactions and only 31% were about scheduling or coordination. Moreover, people rarely switched from IM to another medium when the conversation got complex. We found evidence of two distinct styles of use. Heavy IM users and frequent IM partners mainly used it to work together: to discuss a broad range of topics via many fast-paced interactions per day, each with many short turns and much threading and multitasking. Light users and infrequent pairs mainly used IM to coordinate: for scheduling, via fewer conversations per day that were shorter, slower-paced with less threading and multitasking.

This paper compares the effects of three different dialog initiative strategies (system initiative, mixed initiative and user initiative) on system performance and user acceptance on a large directory information access task. We used a personnel directory query application that could be accessed from a voice-only (telephony) and a multi-modal (kiosk) interface. Although the user initiative condition resulted in a lower proportion of in-grammar utterances, no significant effects of dialog initiative were observed for concept accuracy, perceived task completion, ease of use or user satisfaction. Dialogs were significantly shorter with the kiosk interface than with the telephony interface, and users preferred the kiosk interface and found it easier to use. 1.

This paper describes our progress towards building a telephony-based spoken dialogue system for workshop/conference services. A mixed-initiative dialogue system has been developed that is engineered to o er users natural interaction with the system, ease-of-use and robustness towards ambiguous requests and machine errors. A prototype system, known as W99, is described in this paper

The design of methods for performance evaluation is a major open research issue in the area of spoken language dialogue systems. This paper presents the PARADISE methodology for developing predictive models of spoken dialogue performance, and shows how to evaluate the predictive power and generalizability of such models. To illustrate the methodology, we develop a number of models for predicting system usability (as measured by user satisfaction), based on the application of PARADISE to experimental data from three different spoken dialogue systems. We then measure the extent to which the models generalize across different systems, different experimental conditions, and different user populations, by testing models trained on a subset of the corpus against a test set of dialogues. The results show that the models generalize well across the three systems, and are thus a first approximation towards a general performance model of system usability.

In the future, the world of telecommunications will be vastly different than it is today. The driving force will be the seamless integration of real time communications (e.g. voice, video, music, etc.) and data into a single network, with ubiquitous access to that network anywhere, anytime, and by a wide range of devices. The only currently available ubiquitous access device to the network is the telephone, and the only ubiquitous user access technology mode is spoken voice commands and natural language dialogues with machines. In the future, new access devices and modes will augment speech in this role, but are unlikely to supplant the telephone and access by speech anytime soon. Speech technologies have progressed to the point where they are now viable for a broad range of communications services, including: compression of speech for use over wired and wireless networks; speech synthesis, recognition, and understanding for dialogue access to information, people, and messaging; and speaker verification for secure access to information and services. The paper provides brief overviews of these technologies, discusses some of the unique properties of wireless, plain old telephone service, and Internet protocol networks that make voice communication and control problematic, and describes the types of voice services available in the past and today, and those that we foresee becoming available over the next several years.

This paper describes VPQ (Voice Post Query), a dialog system that provides spoken access to the information in the AT&T corporate personnel database (>120,000 entries). An explicit design goal is to have the user’s initial interaction with the system be rather unconstrained and to rely on tighter, prompt constrained, dialog only when absolutely necessary. The purpose of VPQ is both a) to explore and exploit the capabilities of “state of the art” speech recognition systems for this highperplexity task, and b) to develop the natural language understanding and dialog control components necessary for effective and efficient user interactions. The VPQ task spans a wide range of possible dialog scenarios. They range from simple “one-shot” to complex multi-turn interactions. The former correspond to interactions where the initial utterance is unambiguous and the system’s response appropriately terminates the interaction either by providing the desired information or completing a call to the requested person. The more complex interactions occur primarily whenever ambiguities or errors require resolution. Current speech recognition accuracy of 80% is adequate to pursue such an ambitious task. This paper highlights the inherent challenges in such a task, the major components of the system, the rationale for their design, and how they perform. The VPQ project targets a variety of access devices, including telephony, desktop and handheld devices offering multi-modal user interfaces. In this paper we focus on describing the telephony interface.

One of the challenges for the current state of the art in spoken dialogue systems is how to make the limitations of the system apparent to users. These limitations have many sources: limited vocabulary, limited grammar, or limitations in the application domain. This study explored the use of a 4-minute tutorial session to acquaint novice users with the features of a spoken dialogue system for accessing email. On a set of three scenariobased tasks, novice users who had the tutorial had task completion times and user satisfaction ratings that were comparable to those of expert users of the system. Novices who did not experience the tutorial had significantly longer task completion times on the initial task, but similar completion times to the tutorial group on the final task. User satisfaction ratings of the no-tutorial group were consistently lower than the ratings of the tutorial and the expert groups. Evaluation using the PARADISE [7] framework indicated that perceived task completion, mean recognition score, and number of help requests were significant predictors of user satisfaction with the system. 1.

This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity.

Feedforward neural networks spanning input durations of 35, 65, 125, and 245 ns were trained to classify subword speech segments from continuous utterances into 46 phoneme-like classes. The performance of networks with different input spans showed that brief sounds can be reliably detected by networks with longer input spans, but results for a subset of longer-duration phonemes (diphthongs) indicate that a network needs a wide enough view of the input to capture the salient features of the output class. The best classification performance was observed using a completely connected three-layer network with a 125-ns input span. Performance averaged 56%, ranging from 31% to 82% across phoneme classes using a top-three candidates decision criterion