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首页> 外文期刊>IEICE Transactions on Information and Systems >Noise Robust Speaker Identification Using Sub-band Weighting In Multi-band Approach
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Noise Robust Speaker Identification Using Sub-band Weighting In Multi-band Approach

机译：多频带方法中的子带加权噪声鲁棒说话人识别

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摘要

Recently, many techniques have been proposed to improve speaker identification in noise environments. Among these techniques, we consider the feature recombination technique for the multi-band approach in noise robust speaker identification. The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional feature recombination technique does not provide notable performance improvement compared with the full-band system. Even though the speech is corrupted by the broad-band noise, the degree of the noise corruption on each sub-band is different from each other. In the conventional feature recombination for speaker identification, all sub-band features are used to compute multi-band likelihood score, but this likelihood computation does not use a merit of multi-band approach effectively, even though the sub-band features are extracted independently. Here we propose a new technique of sub-band likelihood computation with sub-band weighting in the feature recombination method. The signal to noise ratio (SNR) is used to compute the sub-band weights. The proposed sub-band-weighted likelihood computation makes a speaker identification system more robust to noise. Experimental results show that the average error reduction rate (ERR) in various noise environments is more than 24% compared with the conventional feature recombination-based speaker identification system.
机译：近来，已经提出了许多技术来改善噪声环境中的说话者识别。在这些技术中，我们考虑在噪声鲁棒的说话人识别中采用多频带方法的特征重组技术。常规特征重组技术在带宽受限的噪声条件下非常有效，但是在宽带噪声条件下，与全频带系统相比，常规特征重组技术无法提供显着的性能提升。即使语音受到宽带噪声的破坏，每个子带上的噪声破坏程度也互不相同。在用于说话人识别的常规特征重组中，所有子带特征都用于计算多带似然度得分，但是即使子带特征是独立提取的，这种似然性计算也没有有效利用多带方法的优点。 。在这里，我们提出了一种在特征重组方法中利用子带加权的子带似然计算新技术。信噪比（SNR）用于计算子带权重。所提出的子带加权似然计算使说话者识别系统对噪声更鲁棒。实验结果表明，与传统的基于特征组合的说话人识别系统相比，在各种噪声环境下的平均错误减少率（ERR）超过24％。

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