Emmanuel Ferreira

• PhD Student
• PhD Student at University of Avignon

This paper investigates some conditions under which polarized user appraisals gathered throughout the course of a vocal interaction between a machine and a human can be integrated in a reinforcement learning-based dialogue manager. More specifically, we discuss how this information can be cast into socially-inspired rewards for speeding up the poli...

Many recent competitive state-of-the-art solutions for understanding of speech data have in common to be probabilistic and to rely on machine learning algorithms to train their models from large amount of data. The difficulty remains in the cost and time of collecting and annotating such data, but also to update the existing models to new condition...

Others can have a different perception of the world than ours. Understanding this divergence is an ability, known as perspective taking in developmental psychology, that humans exploit in daily social interactions. A recent trend in robotics aims at endowing robots with similar mental mechanisms. The goal then is to enable them to naturally and eff...

Many robotic projects use simulation as a faster and easier way to develop, evaluate and validate software components compared with on-board real world settings. In the human-robot interaction field, some recent works have attempted to integrate humans in the simulation loop. In this paper we investigate how such kind of robotic simulation software...

This paper investigates the conditions under which expert knowledge can be used to accelerate the policy optimization of a learning agent. Recent works on reinforcement learning for dialogue management allowed to devise sophisticated methods for value estimation in order to deal all together with exploration/exploitation dilemma, sample-efficiency...

This paper investigates the conditions un- der which social signals (facial expres- sions, postures, gazes, etc.), especially non-verbal multimodal user appraisal, can help to accelerate the learning capacity of a Reinforcement Learning (RL) agent in the dialogue management context. For this purpose a potential-based shaping reward method is used j...

This paper investigates the conditions under which cues from social signals can be used for user adaptation (or user tracking) of a learning agent. In this work we consider the case of the Reinforcement Learning (RL) of a dialogue management module. Social signals (gazes, postures, emotions, etc.) have an undeniable importance in human interactions...

The ability to collect and process a large amount of resources (e.g. vocabularies, text corpora, transcribed speech corpora and phonetic dictionaries) constitutes a critical prerequisite of systems based on statistical methods. This aspect becomes crucial for languages presenting a lack of computer resources, also known as under-resourced languages...

This paper explores a novel method for context-dependent models in automatic speech recognition (ASR), in the context of under-resourced languages. We present a simple way to realize a tying states approach, based on a new vectorial representation of the HMM states. This vectorial representation is considered as a vector of a low number of paramete...

The ability to collect and process a large amount of resources (vocabularies, text corpora, transcribed speech corpora, phonetic dictionaries) constitutes a critical prerequisite of systems based on statistical methods. This problem becomes crucial for languages presenting a lack of computer resources, also known as under-resourced languages, such...