Department of Psychology, 5635University of Minnesota, Minneapolis, MN, USA.
Reality Labs Research, Redmond, WA, USA.
Trends Hear. 2022 Jan-Dec;26:23312165221097789. doi: 10.1177/23312165221097789.
To optimally improve signal-to-noise ratio in noisy environments, a hearing assistance device must correctly identify what is signal and what is noise. Many of the biosignal-based approaches to solving this question are themselves subject to noise, but head angle is an overt behavior that may be possible to capture in practical devices in the real world. Previous orientation studies have demonstrated that head angle is systematically related to listening target; our study aimed to examine whether this relationship is sufficiently reliable to be used in group conversations where participants may be seated in different layouts and the listener is free to turn their body as well as their head. In addition to this simple method, we developed a source-selection algorithm based on a hidden Markov model (HMM) trained on listeners' head movement. The performance of this model and the simple head-steering method was evaluated using publicly available behavioral data. Head angle during group conversation was predictive of active talker, exhibiting an undershoot with a slope consistent with that found in simple orientation studies, but the intercept of the linear relationship was different for different talker layouts, suggesting it would be problematic to rely exclusively on this information to predict the location of auditory attention. Provided the location of all target talkers is known, the HMM source selection model implemented here, however, showed significantly lower error in identifying listeners' auditory attention than the linear head-steering method.
为了在嘈杂环境中优化信噪比，听力辅助设备必须正确识别什么是信号，什么是噪声。许多基于生物信号的方法都受到噪声的影响，但头部角度是一种明显的行为，在实际设备中可能会在现实世界中捕捉到。以前的定向研究表明，头部角度与听力目标系统相关；我们的研究旨在检查这种关系是否足够可靠，以便在组对话中使用，在组对话中，参与者可能以不同的布局就座，并且听众可以自由转动身体和头部。除了这种简单的方法，我们还开发了一种基于隐马尔可夫模型（HMM）的声源选择算法，该算法是针对听众头部运动进行训练的。该模型和简单的头部引导方法的性能使用公开的行为数据进行了评估。组对话期间的头部角度可预测活跃说话者，其表现出与简单定向研究中发现的斜率一致的欠冲，但不同说话者布局的线性关系的截距不同，这表明仅依赖此信息来预测听觉注意力的位置可能会有问题。但是，如果已知所有目标说话者的位置，则这里实施的 HMM 声源选择模型在识别听众的听觉注意力方面的错误明显低于线性头部引导方法。
