Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/2101
Title: | Convolutional bias removal based on normalizing the filterbank spectral magnitude | Authors: | Tüfekçi, Zekeriya | Keywords: | Additive noise Convolutional noise Robust speaker verification Convolution Filter banks |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Source: | Tüfekçi, Z. (2007). Convolutional bias removal based on normalizing the filterbank spectral magnitude. IEEE Signal Processing Letters, 14(7), 485-488. doi:10.1109/LSP.2006.891313 | Abstract: | In this letter, a novel convolutional bias removal technique is proposed. The proposed method is based on scaling the filterbank magnitude by the average of filterbank magnitude over time. The relation between the cepstral mean normalization (CMN) and proposed algorithm is derived. The experimental results show that the proposed algorithm is more robust than the CMN for both convolutional bias and additive noise. For example, the proposed method reduced the equal error rate by 5.66% and 10.16% on average for the convolutional bias and 12-dB additive noise, respectively. | URI: | http://doi.org/10.1109/LSP.2006.891313 http://hdl.handle.net/11147/2101 |
ISSN: | 1070-9908 1070-9908 |
Appears in Collections: | Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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