文章基本信息

标题：Physiologically Motivated Feature Extraction for Robust Automatic Speech Recognition
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作者：Ibrahim Missaoui ; Zied Lachiri
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：4
DOI：10.14569/IJACSA.2016.070438
出版社：Science and Information Society (SAI)
摘要：In this paper, a new method is presented to extract robust speech features in the presence of the external noise. The proposed method based on two-dimensional Gabor filters takes in account the spectro-temporal modulation frequencies and also limits the redundancy on the feature level. The performance of the proposed feature extraction method was evaluated on isolated speech words which are extracted from TIMIT corpus and corrupted by background noise. The evaluation results demonstrate that the proposed feature extraction method outperforms the classic methods such as Perceptual Linear Prediction, Linear Predictive Coding, Linear Prediction Cepstral coefficients and Mel Frequency Cepstral Coefficients.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Feature extraction; Two-dimensional Gabor filters; Noisy speech recognition