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Abstract
English speech recognition experiments are presented employing both: audio signal and Facial Motion Capture (FMC) recordings. The principal aim of the study was to evaluate the influence of feature vector dimension reduction for the accuracy of vocalic segments classification employing neural networks. Several parameter reduction strategies were adopted, namely: Extremely Randomized Trees, Principal Component Analysis and Recursive Parameter Elimination. The feature extraction process is explained, applied feature selection methods are presented and obtained results are discussed
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Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- Multimedia and Network Information Systems : Proceedings of the 11th International Conference MISSI 2018 strony 490 - 500
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Zaporowski S., Czyżewski A..: Selection of Features for Multimodal Vocalic Segments Classification, W: Multimedia and Network Information Systems : Proceedings of the 11th International Conference MISSI 2018, 2018, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-98677-4
- Verified by:
- Gdańsk University of Technology
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