Consumer electronics equipped with a microphone array, such as car navigation devices and headsets commonly implement speech enhancement techniques based on the gradient method to cope with additive noise. However, while these techniques had been originally developed for voice communication and can maximize the signal-to-distortion ratio (SDR), they cannot always maximize automatic speech recognition (ASR) accuracy. For this reason, the front-end speech enhancement parameters have been adjusted by human experts to each environment and acoustic model. In this study, we developed a novel system for maximizing the accuracy of a given ASR engine by automatically adjusting the front-end speech enhancement. The proposed method allows consumers to use ASR through the consumer electronics with less stress when ambient noise varies. A genetic algorithm (GA) is used to generate parameter values of the front-end speech enhancement for particular environments. The generated values can be dynamically assigned to input speech signals by preliminarily clustering the environments based on noise features. In evaluations, parameter values determined by our method outperformed one adjusted by a human expert.

中文翻译：

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语音增强参数调整以最大限度地提高自动语音识别的准确性

配备麦克风阵列的消费电子产品，例如汽车导航设备和耳机，通常采用基于梯度方法的语音增强技术来应对加性噪声。然而，虽然这些技术最初是为语音通信而开发的，并且可以最大限度地提高信号失真比 (SDR)，但它们并不总是能够最大限度地提高自动语音识别 (ASR) 的准确性。出于这个原因，前端语音增强参数已经由人类专家针对每个环境和声学模型进行了调整。在这项研究中，我们开发了一种新颖的系统，通过自动调整前端语音增强来最大限度地提高给定 ASR 引擎的准确性。所提出的方法允许消费者在环境噪声变化时以较小的压力通过消费电子产品使用 ASR。遗传算法（GA）用于为特定环境生成前端语音增强的参数值。通过基于噪声特征对环境进行初步聚类，可以将生成的值动态分配给输入语音信号。在评估中，我们的方法确定的参数值优于人类专家调整的参数值。