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.

中文翻译：

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使用状态空间模型提高线性和非线性方法的听觉注意力解码性能

在助听器应用中识别目标说话者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) 方法从单次试验 EEG 记录中识别目标说话者。AAD 方法基于线性最小二乘成本函数或非线性神经网络从 EEG 记录中重建出席的语音包络，然后直接将重建的包络与说话人的语音包络进行比较，以使用皮尔逊相关系数识别出席的说话人。由于这些相关系数波动很大，为了可靠的解码，使用了大的相关窗口，这会导致大的处理延迟。在本文中，我们使用通过小相关窗口获得的相关系数来研究状态空间模型，以提高线性和非线性 AAD 方法的解码性能。实验结果表明，状态空间模型显着提高了解码性能。