Abstract
Several research studies have shown that the robustness and performance of speech recognition systems can be improved using physiologically inspired filterbank based on Gabor filters. In this paper, we proposed a feature extraction method based on 59 two-dimensional Gabor filterbank. The use of these set of filters aims to extracting specific modulation frequencies and limiting the redundancy on feature level. The recognition performance of our feature extraction method is evaluated in isolated words extracted from TIMIT corpus. The obtained results demonstrate that the proposed extraction method gives better recognition rates to those obtained using the classic methods MFCC, PLP and LPC.
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Missaoui, I., Lachiri, Z. (2014). Gabor Filterbank Features for Robust Speech Recognition. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds) Image and Signal Processing. ICISP 2014. Lecture Notes in Computer Science, vol 8509. Springer, Cham. https://doi.org/10.1007/978-3-319-07998-1_76
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DOI: https://doi.org/10.1007/978-3-319-07998-1_76
Publisher Name: Springer, Cham
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