Despite recent advances in vision-based gesture recognition, its applications remain largely limited to artificially defined and well-articulated gesture signs used for human-computer interaction. A key reason for this is the low recognition rates for "natural" gesticulation. Previous attempts of using speech cues to reduce error-proneness of visual classification have been mostly limited to keyword-gesture coanalysis. Such scheme inherits complexity and delays associated with natural language processing. This paper offers a novel "signal-level" perspective, where prosodic manifestations in speech and hand kinematics are considered as a basis for coanalyzing loosely coupled modalities. We present a computational framework for improving continuous gesture recognition based on two phenomena that capture voluntary (coarticulation) and involuntary (physiological) contributions of prosodic synchronization. Physiological constraints, manifested as signal interruptions during multimodal production, are exploited in an audiovisual feature integration framework using hidden Markov models. Coarticulation is analyzed using a Bayesian network of naive classifiers to explore alignment of intonationally prominent speech segments and hand kinematics. The efficacy of the proposed approach was demonstrated on a multimodal corpus created from the Weather Channel broadcast. Both schemas were found to contribute uniquely by reducing different error types, which subsequently improves the performance of continuous gesture recognition.

Although gesture recognition has been studied extensively, communicative, affective, and biometrical "utility" of natural gesticulation remains relatively unexplored. One of the main reasons for that is the modeling complexity of spontaneous gestures. While lexical information in speech provides additional cues for disambiguating gestures, it does not cover rich paralinguistic domain. This paper offers initial findings from a large corpus of natural monologues about prosodic structuring between frequent beat-like strokes and concurrent speech. Using a set of audio-visual features in an HMM-based formulation, we are able to improve the discrimination between visually similar gestures. Those types of articulatory strokes represent different communicative functions. The analysis is based on the temporal alignment of detected vocal perturbations and the concurrent hand movement. As a supplementary result, we show that recognized articulatory strokes may be used for quantifying gesturing behavior.

Despite recent advances in gesture recognition, reliance on the visual signal alone to classify unrestricted continuous gesticulation is inherently error-prone. Since spontaneous gesticulation is mostly coverbal in nature, there have been some attempts of using speech cues to improve gesture recognition. Some attempts have been made in using speech cues to improve gesture recognition, e.g., keyword-gesture co-analysis. Use of such scheme is burdened by the complexity of natural language understanding. This paper offers a "signal-level" perspective by exploring prosodic phenomena of spontaneous gesture and speech co-production. We present a computational framework for improving continuous gesture recognition based on two phenomena that capture voluntary (co-articulation) and involuntary (physiological) contributions of prosodic synchronization. Physiological constraints, manifested as signal interruptions in multimodal production, are exploited in an audio-visual feature integration framework using hidden Markov models (HMMs). Co-articulation is analyzed using a Bayesian network of naive classifiers to explore alignment of intonationally prominent speech segments and hand kinematics. The efficacy of the proposed approach was demonstrated on a multimodal corpus created from the Weather Channel broadcast. Both schemas were found to contribute uniquely by reducing different error types, which subsequently improves the performance of continuous gesture recognition.

Although recognition of natural speech and gestures have been studied extensively, previous attempts at combining them in a unified framework to boost classification were mostly semantically motivated, e.g., keyword-gesture co-occurrence. Such formulations inherit the complexity of natural language processing. This paper presents a Bayesian formulation that uses a phenomenon of gesture and speech articulation for improving accuracy of automatic recognition of continuous coverbal gestures. The prosodic features from the speech signal were co-analyzed with the visual signal to learn the prior probability of co-occurrence of the prominent spoken segments with the particular kinematical phases of gestures. It was found that the above co-analysis helps in detecting and disambiguating small hand movements, which subsequently improves the rate of continuous gesture recognition. The efficacy of the proposed approach was demonstrated on a large database collected front the weather channel broadcast. This formulation opens new avenues for bottom-up frameworks of multimodal integration.

This paper presents a framework for designing a natural multimodal human computer interaction (HCI) system. The core of the proposed framework is a principled method for combining information derived from audio and visual cues. To achieve natural interaction, both audio and visual modalities are fused along with feedback through a large screen display. Careful design along with due considerations of possible aspects of a systems interaction cycle and integration has resulted in a successful system. The performance of the proposed framework has been validated through the development of several prototype systems as well as commercial applications for the retail and entertainment industry. To assess the impact of these multimodal systems (MMS), informal studies have been conducted. It was found that the system performed according to its specifications in 95% of the cases and that users showed ad-hoc proficiency, indicating natural acceptance of such systems.

This paper presents a multimodal crisis management system (XISM). It employs processing of natural gesture and speech commands elicited by a user to efficiently manage complex dynamic emergency scenarios on a large display. The developed prototype system demonstrates the means of incorporating unconstrained free-hand gestures and speech in a real-time interactive interface. This paper provides insights into the design aspects of the XISM system. In particular, it addresses the issues of extraction and fusion of gesture and speech modalities to allow more natural interactive behavior. Performance characteristics of the current prototype and considerations for future work are discussed. A series of studies indicated positive response with respect to ease of interacting with the current system.