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| Received:September 06, 2005Revised:May 22, 2007 |
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| Using redundant parallel architecture to improve speaker recognition performance |
| Zhengquan QIU, Junxun YIN, Caiyun FAN |
| (School Electronic and Information Engineering, South China University of Technolog, Guangzhou Guangdong 510640, China; School of Mathematical Sciences, South China University of Technology, Guangzhou Guangdong 510640, China) |
| Abstract: |
| In this paper, we propose two kinds of modifications in speaker recognition. First, the correlations between frequency channels are of prime importance for speaker recognition. Some of these correlations are lost when the frequency domain is divided into sub-bands. Consequently we propose a particularly redundant parallel architecture for which most of the correlations are kept. Second, generally a log transformation used to modify the power spectrum is done after the filter-bank in the classical spectrum calculation. We will see that performing this transformation before the filter bank is more interesting in our case. In the processing of recognition, the Gaussian mixture model (GMM) recognition arithmetic is adopted. Experiments on speech corrupted by noise show a better adaptability of this approach in noisy environments, compared with a conventional device, especially when pruning of some recognizers is performed. |
| Key words: Correlations Redundant parallel architecture Log transformation GMM |
