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中文摘要：
在这篇论文，我们在说话者识别建议二种修正。首先，在频率隧道之间的关联具有为说话者识别的主要重要性。当频率领域被划分成亚乐队时，一些这些关联被失去。因而，我们建议大多数关联为被保留的特别地冗余的平行体系结构。第二，通常，过去常修改力量光谱的木头转变在古典光谱计算在过滤器银行以后被做。我们将看到在过滤器银行前执行这转变在我们的情况中更有趣。在处理识别， Gaussian 混合模型(GMM ) 识别算术被采用。讲话的实验由噪音贿赂了这的更好的适应性在吵闹的环境接近的表演，与一台常规设备相比，特别当一些识别器修剪被执行时。
英文摘要：
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, comoared with a conventional device, esoeciallv when oruning of some recognizers is performed.
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