Publication

Visual Speech Recognition using Active Shape Models and Hidden Markov Models

1996
Conference Papers

Abstract

This paper describes a novel approach for visual speech recognition. The shape of the mouth is modelled by an Active Shape Model which is derived from the statistics of a training set and used to locate, track and parameterise the speakerï¿½s lip movements. The extracted parameters representing the lip shape are modelled as continuous probability distributions and their temporal dependencies are modelled by Hidden Markov Models. We present recognition tests performed on a database of a broad variety of speakers and illumination conditions. The system achieved an accuracy of 85.42 % for a speaker independent recognition task of the first four digits using lip shape information only.

Official source

https://infoscience.epfl.ch/entities/publication/d3c18dd8-5bf5-4072-8033-5311f93c5d3a

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1996
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/d3c18dd8-5bf5-4072-8033-5311f93c5d3a

About this result

Related concepts (29)

Pattern recognition

Pattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Speaker recognition

Speaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to speaker recognition or speech recognition. Speaker verification (also called speaker authentication) contrasts with identification, and speaker recognition differs from speaker diarisation (recognizing when the same speaker is speaking).

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