中文摘要：

当前端在语音识别系统处理，象吠叫规模过滤器银行那样的感性的听觉的过滤器银行广泛地被使用。然而，优化过滤器的设计的问题存那在识别任务提供更高的精确性仍然是开的。由于光谱在特征抽取的分析，一个适应乐队过滤器银行(ABFB ) 被介绍。设计为过滤器的频率回答采用灵活带宽和中心频率并且利用基因算法(GA ) 优化设计参数。优化进程被在性能评估循环把前端过滤器银行与后端识别网络相结合认识到。和零十字路口的山峰振幅(ZCPA ) 的 ABFB 的推广作为一个前面过程展示因为光线的基础功能(RBF ) 系统与吠叫规模过滤器银行相比在坚韧性显示出重要改进。在 ABFB，几亚乐队仍然是向更低的频率集中的更多，但是他们的准确地点被表演而非感性的标准决定。为优化的容易，仅仅对称的乐队这里被考虑，它仍然提供令人满意的结果。

英文摘要：

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems. However, the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open. Owing to spectral analysis in feature extraction, an adaptive bands filter bank （ABFB） is presented. The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm （GA） to optimize the design parameters. The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop. The deployment of ABFB together with zero-crossing peak amplitude （ZCPA） feature as a front process for radial basis function （RBF） system shows significant improvement in robustness compared with the Bark-scale filter bank. In ABFB, several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria. For the ease of optimization, only symmetrical bands are considered here, which still provide satisfactory results.