This study analyzes user corrections of system errors in the TOOT spoken dialogue system. We find that corrections differ from noncorrections prosodically, in ways consistent with hyperarticulated speech, although many corrections are not hyperarticulated. Yet both are misrecognized more frequently than non-corrections --- though no more likely to be rejected by the system. Corrections more distant from the error they correct tend to exhibit greater prosodic differences, and also to be recognized more poorly. System dialogue strategy affects users' choice of correction type, suggesting that strategy-specific methods of detecting or coaching users on corrections may be useful. Strategies that produce longer tasks but fewer misrecognitions and subsequent corrections are preferred by users. 1. INTRODUCTION Since spoken dialogue systems often make mistakes in recognizing user input, accurate methods of detecting and correcting system errors are essential to supporting successful interact...

diane/julia¡ This paper focuses on the analysis and prediction of so-called aware sites, defined as turns where a user of a spoken dialogue system first becomes aware that the system has made a speech recognition error. We describe statistical comparisons of features of these aware sites in a train timetable spoken dialogue corpus, which reveal significant prosodic differences between such turns, compared with turns that ‘correct ’ speech recognition errors as well as with ‘normal’ turns that are neither aware sites nor corrections. We then present machine learning results in which we show how prosodic features in combination with other automatically available features can predict whether or not a user turn was a normal turn, a correction, and/or an aware site. 1

sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the How May I Help You spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automaticallyobtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.

Spoken human-machine dialogs are prone to communication failures due to imperfect speech recognition and understanding. In order to recover from these failures, users typically engage in error correction subdialogs. Lengthy error correction subdialogs should be avoided since they increase the overall task completion time and decrease user satisfaction. This study analyzes a large corpus of human-computer dialogs and identifies properties of system responses that affect user frustration and recognition error rates in error correction subdialogs. 1.

This paper introduces Stem-ML, which is a model of the prosody generation process with an associated description language, and suggests how it may help prosody recognition. We applied Stem-ML modeling to three topics: the modeling of prosodic strengths, intonation types, and noun phrase patterns. Stem-ML parameters derived from �� � contours may have a more consistent relationship with prosodic events than raw ��� values. This may improve identification of accent classes, accent strengths, and intonation types.

The problems caused by imperfect speech recognition in spoken dialogue systems are well known: they confound the ability of the system to manage the dialogue, and can lead to both user frustration and task failure. Speech recognition errors are likely to persist for the foreseeable future, and so the development and adoption of a well-founded approach to the handling of error situations may be an important component in achieving general public acceptability for systems of this kind. In this paper, we compare two studies of user behaviour in response to speech recognition errors in quite different dialog applications; the analysis supports the view that user behaviour during error conditions contains a large component that is independent of the domain of the dialogue. The prospect of a consistent response to errors across a wide range of domains enhances the prospects for a general theory of error recognition and repair. 1

This paper deals with user corrections and aware sites of system errors in the TOOT spoken dialogue system. We first describe our corpus, and give details on our procedure to label corrections and aware sites. Then, we show that corrections and aware sites exhibit some prosodic and other properties which set them apart from `normal' utterances.

In this paper we study two types of machine learning techniques, rule-induction and memorybased learning, for error detection in spoken dialogue systems. The learners are trained and tested on two tasks: predicting whether the current user utterance will cause problems, and identifying whether the previous user utterance has caused a problem in the ongoing dialogue. We focus on a variety of features readily available in the majority of spoken dialogue systems: dialogue history, recognized words, and prosodic characteristics of the user input. We find that the learners gain relatively little from the inclusion of prosodic features, even though at first sight the general prosodic trends in our corpus are in agreement with earlier observations from the literature. A closer inspection of the data reveals that the prosodic feature values are highly dependent on the problem’s context, represented by the most recently asked system question type. As a consequence, when separate classifiers are trained on subsets of the data that are split by system question type, the learners profit much more from prosodic information. It is shown that such an informed splitting is beneficial for our other feature sets as well. The consequences of this approach for error detection are discussed.

Spoken dialogue systems promise efficient and natural access to information services from any phone. Recently, spoken dialogue systems for widely used applications such as email, travel information, and customer care have moved from research labs into commercial use. These applications can receive millions of calls a month. This huge amount of spoken dialogue data has led to a need for fully automatic methods for selecting a subset of caller dialogues that are most likely to be useful for further system improvement, to be stored, transcribed and further analyzed. This paper reports results on automatically training a Problematic Dialogue Identifier to classify problematic human-computer dialogues using a corpus of 1242 DARPA Communicator dialogues in the travel planning domain. We show that using fully automatic features we can identify classes of problematic dialogues with accuracies from 67% to 89%.

Surface-marker-based dialog modelling: