Class Confusability Reduction in Audio-Visual Speech Recognition Using Random Forests
| Autor: | Gonzalo D. Sad, Juan Carlos Gómez, Lucas D. Terissi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Reduction (complexity)
030507 speech-language pathology & audiology 03 medical and health sciences Computer science Speech recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Audio-visual speech recognition 02 engineering and technology 0305 other medical science Speech classification Class (biology) Random forest |
| Zdroj: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783319751924 CIARP |
| DOI: | 10.1007/978-3-319-75193-1_70 |
| Popis: | This paper presents an audio-visual speech classification system based on Random Forests classifiers, aiming to reduce the intra-class misclassification problems, which is a very usual situation, specially in speech recognition tasks. A novel training procedure is proposed, introducing the concept of Complementary Random Forests (CRF) classifiers. Experimental results over three audio-visual databases, show that a good performance is achieved with the proposed system for the different types of input information considered, viz., audio-only information, video-only information and fused audio-video information. In addition, these results also indicate that the proposed method performs satisfactorily over the three databases using the same configuration parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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