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首页> 外文期刊>Intelligent Transport Systems, IET >Speech enhancement for in-vehicle voice control systems using wavelet analysis and blind source separation
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Speech enhancement for in-vehicle voice control systems using wavelet analysis and blind source separation

机译：使用小波分析和盲源分离的车载语音控制系统的语音增强

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摘要

Multiple sources of interference and low signal-to-interference ratio are two major challenges to speech-based intelligent driver assistant systems. They will have a serious impact on the performance of voice control commands. To solve this problem, this study proposes a speech enhancement method based on wavelet analysis and blind source separation in a complicated automobile environment. Firstly, according to the characteristics of typical speech signals, an automatic selection method of optimal wavelet basis is given to optimise the signal denoising performance. Secondly, the mixed signals are separated by a complex fast-independent component analysis (ICA) algorithm, and then the inverse short-time Fourier transform is utilised to obtain the separated signals in time domain. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method. Results show that its performance in terms of a correlation coefficient can be improved by about 7% compared with that of the conventional method only using fast-ICA.
机译：多种干扰源和低信令到干扰比是基于语音的智能驱动器助手系统的两个主要挑战。它们将对语音控制命令的表现产生严重影响。为了解决这个问题，本研究提出了一种基于小波分析和复杂的汽车环境中的盲源分离的语音增强方法。首先，根据典型语音信号的特征，给出了最佳小波的自动选择方法来优化信号去噪性能。其次，混合信号通过复杂的快速独立分量分析（ICA）算法分离，然后利用逆短时傅立叶变换来获得时间域中的分离信号。最后，进行了实验以证明该方法的有效性。结果表明，与仅使用FAST-ICA的传统方法相比，其在相关系数方面的性能可以提高约7％。

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