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Abstract
An elementary visual unit – the viseme is concerned in the paper in the context of preparing the feature vector as a main visual input component of Audio-Visual Speech Recognition systems. The aim of the presented research is a review of various approaches to the problem, the implementation of algorithms proposed in the literature and a comparative research on their effectiveness. In the course of the study an optimal feature vector construction and an appropriate selection of the classifier were sought. The experimental research was conducted on the basis of a spoken corpus in which speech was represented both acoustically and visually. The extracted features represented three types: geometrical, textural and mixed ones. The features were processed employing the classification algorithms based on Hidden Markov Models and Sequential Minimal Optimization. Tests were carried out employing the processed video material recorded with English native speakers who read specially prepared list of commands. The obtained results are discussed in the paper.
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/s11042-017-5217-5
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
MULTIMEDIA TOOLS AND APPLICATIONS
no. 77,
pages 16495 - 16532,
ISSN: 1380-7501 - Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- JACHIMSKI D., Czyżewski A., Ciszewski T.: A comparative study of English viseme recognition methods and algorithms// MULTIMEDIA TOOLS AND APPLICATIONS. -Vol. 77, iss. 13 (2018), s.16495-16532
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/s11042-017-5217-5
- Verified by:
- Gdańsk University of Technology
Referenced datasets
- dataset MODALITY corpus - SPEAKER 35 - COMMANDS C1
- dataset MODALITY corpus - SPEAKER 21 - SEQUENCE S6
- dataset MODALITY corpus - SPEAKER 21 - COMMANDS C5
- dataset MODALITY corpus - SPEAKER 21 - SEQUENCE S4
- dataset MODALITY corpus - SPEAKER 10 - SEQUENCE S1
- dataset MODALITY corpus - SPEAKER 01 - SEQUENCE S2
- dataset MODALITY corpus - SPEAKER 39 - COMMANDS C1
- dataset MODALITY corpus - SPEAKER 01 - SEQUENCE S3
- dataset MODALITY corpus - SPEAKER 01 - COMMANDS C3
- dataset MODALITY corpus - SPEAKER 21 - SEQUENCE S2
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