SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Spot the conversation: speaker diarisation in the wild

The goal of this paper is speaker diarisation of videos collected 'in the wild'. We make three key contributions. First, we propose an automatic audio-visual diarisation method for YouTube videos. Our method consists of active speaker detection using audio-visual methods and speaker verification using self-enrolled speaker models. Second, we integrate our method into a semi-automatic dataset creation pipeline which significantly reduces the number of hours required to annotate videos with diarisation labels. Finally, we use this pipeline to create a large-scale diarisation dataset called VoxConverse, collected from 'in the wild' videos, which we will release publicly to the research community. Our dataset consists of overlapping speech, a large and diverse speaker pool, and challenging background conditions.Comment: The dataset will be available for download from http://www.robots.ox.ac.uk/~vgg/data/voxceleb/voxconverse.html . The development set will be released in July 2020, and the test set will be released in October 202

Predicting the birth of a spoken word

Children learn words through an accumulation of interactions grounded in context. Although many factors in the learning environment have been shown to contribute to word learning in individual studies, no empirical synthesis connects across factors. We introduce a new ultradense corpus of audio and video recordings of a single child’s life that allows us to measure the child’s experience of each word in his vocabulary. This corpus provides the first direct comparison, to our knowledge, between different predictors of the child’s production of individual words. We develop a series of new measures of the distinctiveness of the spatial, temporal, and linguistic contexts in which a word appears, and show that these measures are stronger predictors of learning than frequency of use and that, unlike frequency, they play a consistent role across different syntactic categories. Our findings provide a concrete instantiation of classic ideas about the role of coherent activities in word learning and demonstrate the value of multimodal data in understanding children’s language acquisition

New horizons in the study of child language acquisition

URL to paper on conference site.Naturalistic longitudinal recordings of child development promise to reveal fresh perspectives on fundamental questions of language acquisition. In a pilot effort, we have recorded 230,000 hours of audio-video recordings spanning the first three years of one child's life at home. To study a corpus of this scale and richness, current methods of developmental cognitive science are inadequate. We are developing new methods for data analysis and interpretation that combine pattern recognition algorithms with interactive user interfaces and data visualization. Preliminary speech analysis reveals surprising levels of linguistic fine-tuning by caregivers that may provide crucial support for word learning. Ongoing analyses of the corpus aim to model detailed aspects of the child's language development as a function of learning mechanisms combined with lifetime experience. Plans to collect similar corpora from more children based on a transportable recording system are underway.National Science Foundation (U.S.)MIT Center for Future BankingMassachusetts Institute of Technology. Media LaboratoryUnited States. Office of Naval ResearchUnited States. Dept. of Defens