In general, the background noise degrades the speech quality. Thus, the intelligibility of the speech can be enhanced by mitigating the effects of background noise and echo suppression. So, speech enhancement can also be viewed as one of the optimization problems. In this work, directed search optimization (DSO) method is used to enhance the speech quality which is originally degraded. The performance of DSO-based speech enhancement method is compared with particle swarm optimization (PSO) and least mean square (LMS)-based methods in terms of output average segmental SNR and speech quality. From the experimental results, it was observed that the output spectrogram, output ASSNR and speech quality using DSO algorithm are far better as compared to PSO and LMS-based methods. Moreover, DSO-based method is computationally less complex as compared to the PSO-based method.

中文翻译：

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基于定向搜索优化的语音增强技术

通常，背景噪声会降低语音质量。因此，可以通过减轻背景噪声和回声抑制的影响来增强语音的可理解性。因此，语音增强也可以看作是优化问题之一。在这项工作中，使用定向搜索优化（DSO）方法来增强原本退化的语音质量。将基于 DSO 的语音增强方法与基于粒子群优化 (PSO) 和基于最小均方 (LMS) 的方法在输出平均分段 SNR 和语音质量方面的性能进行了比较。从实验结果可以看出，与基于 PSO 和 LMS 的方法相比，使用 DSO 算法的输出频谱图、输出 ASSNR 和语音质量要好得多。而且，