论文标题
使用状态空间模型改善线性和非线性方法的听觉注意力解码性能
Improving auditory attention decoding performance of linear and non-linear methods using state-space model
论文作者
论文摘要
在助听器应用中确定目标发言人对于提高语音理解至关重要。脑电图（EEG）的最新进展表明，可以使用听觉注意解码（AAD）方法从单审EEG记录中识别目标扬声器。 AAD方法基于线性最小二乘成本函数或非线性神经网络，从脑电图录音中重建了所召集的语音信封，然后直接将重建的信封与扬声器的语音信封进行比较，以使用Pearson相关系数来识别所段的讲话者。由于这些相关系数高度波动，因此使用可靠的解码大型相关窗口，这会导致较大的处理延迟。在本文中，我们使用使用一个小相关窗口获得的相关系数研究了状态空间模型，以改善线性和非线性AAD方法的解码性能。实验结果表明，状态空间模型可显着提高解码性能。
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using auditory attention decoding (AAD) methods. AAD methods reconstruct the attended speech envelope from EEG recordings, based on a linear least-squares cost function or non-linear neural networks, and then directly compare the reconstructed envelope with the speech envelopes of speakers to identify the attended speaker using Pearson correlation coefficients. Since these correlation coefficients are highly fluctuating, for a reliable decoding a large correlation window is used, which causes a large processing delay. In this paper, we investigate a state-space model using correlation coefficients obtained with a small correlation window to improve the decoding performance of the linear and the non-linear AAD methods. The experimental results show that the state-space model significantly improves the decoding performance.
