Smile is a key feature in development and can also be heard in the voice. During development, an improvement of vocal emotion recognition abilities has been described along with global neuroanatomical maturation of auditory cortex. While it has been suggested by the predictive coding framework that internal representation refines with age during development, internal representation of vocal smile has not been characterized in children within this specific age range. The present study aims to bridge this knowledge gap by investigating internal representation of vocal smile in children, along with the robustness of this representation. Vocal smile model was assessed in 46 children aged 8 to 12 years and 42 adults. A reverse correlation approach was implemented allowing to distinguish two components of vocal smile model: the acoustic features underlying a vocal smile and the internal representation robustness, quantified as internal Noise. As a group, children display the same internal representation of vocal smile as adults. However, when comparing children’s individual representations to adults one, we demonstrated that within children group, their representation becomes increasingly closer to adults’ template with age. Furthermore, internal noise estimation allows to distinguish two children subgroup, with one subgroup exhibiting a delay in the acquisition of that internal representation. This study provides key insights into the development and variability of emotional voice processing in school-age children.

We demonstrate EmojiVoice, a free, customizable text-to-speech (TTS) toolkit for expressive speech on social robots. We demonstrate our voices through storytelling. This task is aimed to be deployed in classrooms, or libraries where the robot can read a story out loud to children. Moreover, we introduce adaptive clarity to to noisy environments and those with reduced comprehension ability. This storytelling robot voice allows us to demonstrate how, using our light weight and customizable TTS, we are able to have a voice that is expressive, engaging, clear and socially appropriate for the task, improving interactions with and perceptions of social robots.

Research in affective computing and cognitive science has shown the importance of emotional facial and vocal expressions during human-computer and human-human interactions. But, while models exist to control the display and interactive dynamics of emotional expressions, such as smiles, in embodied agents, these techniques can not be applied to video interactions between humans. In this work, we propose an audiovisual smile transformation algorithm able to manipulate an incoming video stream in real-time to parametrically control the amount of smile seen on the user's face and heard in their voice, while preserving other characteristics such as the user's identity or the timing and content of the interaction. The transformation is composed of separate audio and visual pipelines, both based on a warping technique informed by real-time detection of audio and visual landmarks. Taken together, these two parts constitute a unique audiovisual algorithm which, in addition to providing simultaneous real-time transformations of a real person's face and voice, allows to investigate the integration of both modalities of smiles in real-world social interactions.

Electronic Music Distribution is in need of robust and automatically extracted music descriptors. An important attribute of a piece of polyphonic music is what is commonly referred to as "the way it sounds". While there has been a large quantity of research done to model the timbre of individual instruments, little work has been done to analyze "real world" timbre mixtures such as the ones found in popular music. In this paper, we present our research about such "polyphonic timbres". We describe an effective way to model the textures found in a given music signal, and show that such timbre models provide new solutions to many issues traditionally encountered in music signal processing and music information retrieval. Notably, we describe their applications for music similarity, segmentation and pattern induction.

Abstract The intentionally ambiguous expression “Popular Music Browser” reflects the two main goals of this project, which started in 1998, at Sony Computer Science Laboratories. First, we are interested in human‐centered issues related to browsing “Popular Music.” Popular here means that the music accessed is distributed widely and known to many listeners. Second, we consider “popular browsing” of music, i.e., making music accessible to nonspecialists (music lovers) and allowing sharing of musical tastes and information within communities, departing from the usual, single‐user view of digital libraries. This research project covers all areas of the music‐to‐listener chain, from music description, descriptor extraction from the music signal, or data mining techniques, similarity‐based access, and novel music retrieval methods such as automatic sequence generation, and to user interface issues. This article describes the scientific and technical issues at stake and the results obtained, and is illustrated by prototypes developed within the European IST projectCuidado.

The issue of generating automatically sequences of music titles that satisfy arbitrary criteria such as user preferences has gained interest recently, because of the numerous applications in the field of electronic music distribution. All the approaches proposed so far suffer from two main drawbacks: reduced expressiveness and incapacity to handle large music catalogues. We present in this paper a system that is able to produce automatically music playlists out of large, real catalogues (up to 200000 titles), and that can handle arbitrarily complex criteria. We describe the basic algorithm and its adaptation to playlist generation, and report on experiments performed in the context of the European project Cuidado.