In pattern recognition, the need to quantify the quality of a classifier's output has gained importance in the past years. Speaker verification is no exception. This paper presents a probabilistic reliability framework incorporating signal-domain information into the confidence estimation and contrasts this method with classical approaches to estimating the confidence in a given speaker verification classifier output. We show that the method proposed can deal with adverse acoustic conditions for a wide range of signal-to-noise ratios, does not depend on a Gaussian assumption for impostor and client score distributions, and presents benefits in terms of scalability and interpretability of the measure. We contrast reliability and confidence approaches, and evaluate performance on a degraded version of the 295-users XM2VTS database

In this paper, a probabilistic measure for reliability of speaker verification under noisy acoustic conditions is proposed. A Bayesian network is used to estimate a probability for verification errors, given the GMM-based speaker verification system output and additional information about the level of acoustic noise. In particular, the log-likelihood ratio and a signal-to-noise related feature are used to account for the adverse acoustic conditions. The probabilistic measure is subsequently employed in governing a repair sequence of trials for acquiring additional speech presentations which are less likely to lead to unreliable verification. The potential of the proposed method is tested through cross-validation experiments. Finally, the benefits of the repair sequence in terms of verification accuracy is evaluated on a noisy environment speaker verification task.

The main task of a voice-enabled tour-guide robot in mass exhibition setting is to engage visitors in dialogue and provide as much exhibit information as possible in a limited time. In managing such a dialogue, extracting the user (visitor) goal or intention at each dialogue state is the key issue. In mass exhibition conditions uncooperative visitors and speech recognition limitations in noisy acoustic conditions may jeopardize user goal identification. In this paper, we introduce the use of sequential dialogue repair techniques, exploiting the inherent multimodality of the tour-guide robot, in order to reduce the risk of the resulting communication failures. Bayesian networks fusing acoustic and non-acoustic modalities during user goal identification serve as input to graphical models known as decision networks. Decision networks allow the definition of dialogue repair sequences as actions, and provide a decision-theoretic utility-based strategy for selecting actions. The benefits of the proposed repair strategies are demonstrated through experiments with the dialogue system of RoboX, a tour-guide robot successfully deployed at the Swiss National Exhibition (Expo. 02).

In this paper, we propose a method based on Bayesian networks for interpretation of multimodal signals used in the spoken dialogue between a tour-guide robot and visitors in mass exhibition conditions. We report on experiments interpreting speech and laser scanner signals in the dialogue management system of the autonomous tour-guide robot RoboX, successfully deployed at the Swiss National Exhibition (Expo.02). A correct interpretation of a user s (visitor s) goal or intention at each dialogue state is a key issue for successful voice-enabled communication between tour-guide robots and visitors. To infer the visitors goal under the uncertainty intrinsic to these two modalities, we introduce Bayesian networks for combining noisy speech recognition with data from a laser scanner, which is independent of acoustic noise. Experiments with real data, collected during the operation of RoboX at Expo.02 demonstrate the effectiveness of the approach.

In this paper, we propose a Bayesian network framework for managing interactivity between a tour-guide robot and visitors in mass exhibition conditions, through robust interpretation of multi-modal signals. We report on methods and experiments interpreting speech and laser scanner signals in the spoken dialogue management system of the autonomous tour-guide robot RoboX, successfully deployed at the Swiss National Exhibition (Expo.02). A correct interpretation of a user's (visitor's) goal or intention at each dialogue state is a key issue for successful speech-based interaction in voice-enabled communication between robots and visitors. We introduce a Bayesian network approach for combining noisy speech recognition results with noise-independent data from a laser scanner, in order to infer the visitors' goal under the uncertainty intrinsic to these two modalities. We demonstrate the effectiveness of the approach by simulation based on real observations during experiments with the tour-guide robot RoboX at Expo.02.

This paper considers design methodologies in order to develop voice-enabled interfaces for tour-guide robots to be deployed at the Robotics Exposition of the Swiss National Exhibition (Expo.02). Human-robot voice communication presents new challenges for the design of fully autonomous mobile robots, in that interactivity must be robot-initiated in conversation and within a dynamic adverse environment. We approached these general problems for a voice enabled interface, tailored to limited computational resources of one on-board processor, when integrating smart speech signal acquisition, automatic speech recognition and synthesis, as well as dialogue system into the multi-modal, multisensor interface for the Expo tour-guide robot. We also focus on particular issues that need to be addressed in voice-based interaction when planning specific tasks and research experiments for Expo.02 where tour-guide robots will interact with hundred of thousands of visitors for six months, seven days a week, ten hours per day.