Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks

Sri Harsha Dumpala, I. Sheikh, Rupayan Chakraborty, Sunil Kumar Kopparapu
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引用次数: 2

Abstract

Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems. In this paper, we propose a front-end based on Cycle-Consistent Generative Adversarial Network (CycleGAN) which transforms naturally perturbed speech into normal speech, and hence improves the robustness of an ASR system. The CycleGAN model is trained on non-parallel examples of perturbed and normal speech. Experiments on spontaneous laughter-speech and creaky voice datasets show that the performance of four different ASR systems improve by using speech obtained from CycleGAN based front-end, as compared to directly using the original perturbed speech. Visualization of the features of the laughter perturbed speech and those generated by the proposed front-end further demonstrates the effectiveness of our approach.
利用周期一致生成对抗网络提高ASR对扰动语音的鲁棒性
由说话者的情绪和身体状态引起的音频信号中自然引入的扰动会显著降低自动语音识别(ASR)系统的性能。在本文中,我们提出了一种基于循环一致性生成对抗网络(CycleGAN)的前端,将自然扰动语音转换为正常语音,从而提高了ASR系统的鲁棒性。CycleGAN模型是在干扰和正常语音的非并行示例上训练的。在自发笑声语音和吱吱声数据集上的实验表明,与直接使用原始扰动语音相比,使用基于CycleGAN的前端语音可以提高四种不同的ASR系统的性能。对笑声干扰语音的特征和由所提出的前端产生的特征的可视化进一步证明了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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