Hidden Markov Model for Gesture Recognition

Yang, Jie; Xu, Yangsheng

doi:10.21236/ada282845

Cited by 81 publications

(32 citation statements)

References 14 publications

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“…A Hidden Markov model [20] is a collection of finite states connected by transitions. Each state is characterized by two sets of probabilities: a transition probability and either a discrete output probability distribution or continuous output probability density function which, given the state, defines the condition probability of emitting each output symbol from a finite alphabet or a continuous random vector.…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

A Review on Indian Sign Language Recognition

Nair¹,

Bindu²

2013

IJCA

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Section: Hidden Markov Modelsmentioning

confidence: 99%

A Review on Indian Sign Language Recognition

Nair¹,

Bindu²

2013

IJCA

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“…While these approaches have produced highly accurate systems capable of recognizing gestures [11], we are more concerned about important characteristic of approaches such as extensibility and user-dependency rather than only the accuracy.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Glove-Based Gesture Recognition

Parvini

McLeod

Shahabi

et al. 2009

Human-Computer Interaction. Novel Interaction Methods and Techniques

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“…Similarly 31 occurrences of "right" had accompanying gestures, while "straight" had associated gestures 32 times throughout the database. Other candidate words included "across", "no", and "down", but these were dismissed as having too few gesture-marked occurrences (8,8, and 6 respectively).…”

Section: Motivations and Initial Observationsmentioning

confidence: 99%

“…Yang et. al., summarizes these similarities in [8]. HMMs have been applied to the speech recognition problem to partition every word into a finite number of speech elements called phonemes.…”

Section: Gestural Event Detectionmentioning

confidence: 99%

Combined Gesture-Speech Analysis and Speech Driven Gesture Synthesis

Sargin

Aran

Karpov

et al. 2006

2006 IEEE International Conference on Multimedia and Expo

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Multimodal speech and speaker modeling and recognition are widely accepted as vital aspects of state of the art human-machine interaction systems. While correlations between speech and lip motion as well as speech and facial expressions are widely studied, relatively little work has been done to investigate the correlations between speech and gesture. Detection and modeling of head, hand and arm gestures of a speaker have been studied extensively and these gestures were shown to carry linguistic information. A typical example is the head gesture while saying "yes/no". In this study, correlation between gestures and speech is investigated. In speech signal analysis, keyword spotting and prosodic accent event detection has been performed. In gesture analysis, hand positions and parameters of global head motion are used as features. The detection of gestures is based on discrete predesignated symbol sets, which are manually labeled during the training phase. The gesture-speech correlation is modelled by examining the co-occurring speech and gesture patterns. This correlation can be used to fuse gesture and speech modalities for edutainment applications (i.e. video games, 3-D animations) where natural gestures of talking avatars is animated from speech. A speech driven gesture animation example has been implemented for demonstration.

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Hidden Markov Model for Gesture Recognition

Cited by 81 publications

References 14 publications

A Review on Indian Sign Language Recognition

A Review on Indian Sign Language Recognition

An Approach to Glove-Based Gesture Recognition

Combined Gesture-Speech Analysis and Speech Driven Gesture Synthesis

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