Sound Source Selection Based on Head Movements in Natural Group Conversation.

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.