This paper describes an audio-visual speech recognition system based on wavelets and Random Forests. Wavelet multiresolution analysis is used to represent in a compact form the sequence of both acoustic and visual input parameters. Then, recognition is performed using Random Forests classification using the wavelet-based features as inputs. The efficiency of the proposed speech recognition scheme is evaluated over two audio-visual databases, considering acoustic noisy conditions. Experimental results show that a good performance is achieved with the proposed system, outperforming the efficiency of traditional Hidden Markov Model-based approaches. The proposed system has only one tuning parameter, however, experimental results also show that this parameter can be selected within a small range without significantly changing the recognition results.
CITATION STYLE
Terissi, L. D., Sad, G. D., Gómez, J. C., & Parodi, M. (2015). Audio-visual speech recognition scheme based on wavelets and random forests classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9423, pp. 567–574). Springer Verlag. https://doi.org/10.1007/978-3-319-25751-8_68
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