Human-aware navigation and Social Navigation are growing fields of robotics, attempting to tackle challenging navigation problems in human environments. One difficult aspect is understanding how a mobile robot's navigation behavior impacts the way they are perceived by humans, and how they interact together. In our previous work, we proposed an adaptable navigation architecture. In this paper, we describe how we determine which motion variables should be controlled by our architecture. We take a bottom-up approach by determining which primitive notions of locomotion and appearance have the most impact on people's perceptions of a mobile robot. We present the results of two online and one in-person experiment showing that the manner in which a mobile robot navigates has a significant impact on people's attribution of attitudes and physical properties to the robot.

Social Navigation methods attempt to integrate knowledge from Human Sciences fields such as the notion of Proxemics into mobile robot navigation. They are often evaluated in simulations, or lab conditions with informed participants, and studies of the impact of the robot behavior on humans are rare. Humans communicate and interact through many vectors, among which are motion and positioning, which can be related to social hierarchy and the socio-physical context. If a robot is to be deployed among humans, the methods it uses should be designed with this in mind. This work acts as the first step in an ongoing project in which we explore how to design navigation methods for mobile robots destined to be deployed among humans. We aim to consider navigation as more than just a functionality of the robot, and to study the impact of robot motion on humans. In this paper, we focus on the person-following task. We selected a state of the art person-following method as the basis for our method, which we modified and extended in order for it to be more general and adaptable. We conducted pilot experiments using this method on a real mobile robot in ecological contexts. We used results from the experiments to study the Human-Robot Interaction as a whole by analysing both the person-following method and the human behavior. Our preliminary results show that the way in which the robot followed a person had an impact on the interaction that emerged between them.

Aporia is a pluridisciplinary adaptation of a play of Bernard-Marie Koltès, led by plastic artist Alain Quercia in a live performance mixing different Art expressions, Scientific theories and Technologies. To propose original reflections about "otherness", the unique actor of this project embodies in turn all the characters of the play. His performance is supported by real-time pitch-shifting software, which allows him to modify his own voice without affecting its emotional details. These voice changes are remotely controlled by a smart sculpture integrated to the staging. In this paper, we present the used voice processing tool and the iterative co-design of the remote controller in collaboration with the director of Aporia.

This work investigated crosslinguistic perception of Mandarin utterances conveying six classes of attitudes, i.e., dominant/ submissive, friendly/hostile, polite/rude, serious/joking, praising/blaming, and sincere/insincere. Five groups of subjects were tested: native Mandarin speakers, Japanese L2 learners of Mandarin, French L2 learners of Mandarin, naive Japanese without Mandarin ability, and naive French without Mandarin ability. A set of Mandarin attitudinal utterances elicited in role-play dialogues were used as stimuli. Perceptual experiments showed that native subjects performed the best in identifying attitudes, and L2 learners judged better than naive foreigners. A complex interaction was found between attitude and the listener’s L1/L2 experience which was also closely related to culture. Also, the correlations between perceptual patterns and prosodic features were examined, and the results suggested that the prosodic cues for certain attitudes might be dependent on the listener’s language/culture background.

International audience

International audience

International audience

International audience

This paper introduces a new model-constrained, datadriven method to generate prosody from metalinguistic information. We refer here to the general ability of intonation to demarcate speech units and convey information about the propositional and interactional functions of these units within the discourse. Our strong hypotheses are that (1) these functions are directly implemented as prototypical prosodic contours that are coextensive to the unit(s) they apply to, (2) the prosody of the message is obtained by superposing and adding all the contributing contours. We describe here an analysis-by-synthesis scheme that consists in identifying these prototypical contours and separating out their contributions in the prosodic contours of the training data. We will show that such a trainable prosodic model generates faithful prosodic contours with very few prototypical movements.

In the missing data approach to robust Automatic Speech Recognition (ASR), time-frequency regions which carry reliable speech information are identified. Recognition is then based on these regions alone. In this paper, we address the problem of identifying reliable regions and propose two criteria to solve this based on negative energy ( $ s < 0 ) and SNR ( $ s s n 2 2 2 < + ). These criteria are evaluated on the TIDigits corpus for several noise sources and compared with spectral subtraction. We show that in this task the missing data method performs considerably better than spectral subtraction and the combination of the two techniques outperforms either technique used alone. We report robust performance at 0dB SNR for car noise and 10dB SNR for factory noise.

We present here a trainable generative model of French prosody. We focus on the sentence level and design SNNs able to generate both rhythmic and intonation contours for diverse attitudes. First results of a perceptual test show that listeners are able to retrieve the right definition of attitudes by listening to synthetic PSOLA stimuli.

A data-driven method based on a new paradigm is introduced in this paper. We assume that cognitive representations of the discourse are prosodically encoded by means of global multiparametric prototypes. The generation of adequate prosodic contours is then obtained by retrieving and combining these elementary prototypic contours accessed by linguistic or paralinguistic keys. We examine here F0 generation. Two procedures have been applied: the first consists of a superposition model via a structured lexicon, the second uses a recurrent neural network. Preliminary experiments shows that both methods can lead to good quality F0 contours. 1. INTRODUCTION Two main approaches are currently used to describe intonation: . The classical method lies in building phonological representations from phonetic constructs with a bottom-up analysis. These phonetic constructs include both global and local elements. Global elements consist of templates such as declination lines [1] or tonal grids [2]. L...