We investigate the objective performance of five high-end commercially available Hearing Aid (HA) devices compared to DNN-based speech enhancement algorithms in complex acoustic environments. To this end, we measure the HRTFs of a single HA device to synthesize a binaural dataset for training two state-of-the-art causal and non-causal DNN enhancement models. We then generate an evaluation set of realistic speech-in-noise situations using an Ambisonics loudspeaker setup and record with a KU100 dummy head wearing each of the HA devices, both with and without the conventional HA algorithms, applying the DNN enhancers to the latter. We find that the DNN-based enhancement outperforms the HA algorithms in terms of noise suppression and objective intelligibility metrics.

翻译：我们研究了在复杂声学环境中五款高端商用助听器设备与基于深度神经网络的语音增强算法的客观性能。为此，我们测量了单个助听器设备的头相关传输函数，以合成双耳数据集，用于训练两种最先进的因果和非因果DNN增强模型。随后，我们使用Ambisonics扬声器阵列生成逼真的噪声环境语音评估集，并采用佩戴各助听器设备的KU100仿真人头进行录制（分别记录启用和关闭传统助听算法的情况），将DNN增强器应用于后者。研究发现，基于DNN的增强技术在噪声抑制和客观可懂度指标上均优于助听器算法。