An advanced real-time system for gesture recognition is presented , which is able to recognize complex dynamic gestures, such as "hand waving", "spin", "pointing", and "head moving". The recognition is based on global motion features, extracted from each diierence image of the image sequence. The system uses Hidden Markov Models (HMMs) as statistical classiier. These HMMs are trained on a datab...

The ever growing use of gestures in advanced technologies, such as augmented or virtual reality environments, requires more and more understanding of the different levels of representation of gestures, from meanings to motion involving causal physical and biological phenomenon. This is even more true for gestures used for artistic expression, such as dance or musical gestures, or for communicat...

We review a number of examples in which there appear to be “quantal” attributes in functions that relate positions or states of articulators and the acoustic and perceptual consequences of these actions. As a consequence of this review, we have attempted to specify more clearly what defines a quantal relation: the speech production system can assume a set of discrete states such that there are ...

Neuro-physical investigations [1] hint to a new paradigm for feature extraction not used in ASR. This paradigm is based on synchronized brain to brain oscillations, active during speech production and speech perception. This mechanism leads to an evolving theory, the author calls the Unified Theory of Human Speech Processing (UTHSP). The core elements of this theory are the articulatory rhythm ...

Several methods exist for manipulating spectral models either by applying transformations via higher level features or by providing in-depth offline editing capabilities. In contrast, our system aims for direct, full, intuitive, real-time control without exposing any spectral model features to the user. The system extends upon previous machine learning work in gesture-synthesis mapping by apply...

Hand gestures are a sort of nonverbal communication that may be utilized for many diverse purposes, including deaf-mute interaction, robotic manipulation, human-computer interface (HCI), residential management, and healthcare usage. Moreover, most current research uses the artificial intelligence approach effectively to extract dense features from hand gestures. Since them used neural network m...

This article discusses the problem of one-shot gesture recognition using a humancentered approach and its potential application to fields such as human–robot interaction where the user’s intentions are indicated through spontaneous gesturing (one shot). Casual users have limited time to learn the gestures interface, which makes one-shot recognition an attractive alternative to interface customi...

A novel method is described for robot gestures and utterances during a dialogue based on the listener’s understanding and interest, which are recognized from back-channels and head gestures. “Back-channels” are defined as sounds like ‘uhhuh’ uttered by a listener during a dialogue, and “head gestures” are defined as nod and tilt motions of the listener’s head. The back-channels are recognized u...

Children with autism use hand taking and hand leading gestures to interact with others. This is traditionally considered to be an example of atypical behaviour illustrating the lack of intersubjective understanding in autism. However the assumption that these gestures are atypical is based upon scarce empirical evidence. In this paper I present detailed observations in children with autism and ...

This report presents a method for developing a gesture-based system using a multi-dimensional hidden Markov model (HMM). Instead of using geometric features, gestures are converted into sequential symbols. HMMs are employed to represent the gestures and their parmeters are learned from the training data. Based on “the most likely performance” criterion, the gestures can be recognized through ev...