1 Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Trends Hear. 2017 Jan-Dec;21:2331216517710354. doi: 10.1177/2331216517710354.
The masking release (MR; i.e., better speech recognition in fluctuating compared with continuous noise backgrounds) that is evident for listeners with normal hearing (NH) is generally reduced or absent for listeners with sensorineural hearing impairment (HI). In this study, a real-time signal-processing technique was developed to improve MR in listeners with HI and offer insight into the mechanisms influencing the size of MR. This technique compares short-term and long-term estimates of energy, increases the level of short-term segments whose energy is below the average energy, and normalizes the overall energy of the processed signal to be equivalent to that of the original long-term estimate. This signal-processing algorithm was used to create two types of energy-equalized (EEQ) signals: EEQ1, which operated on the wideband speech plus noise signal, and EEQ4, which operated independently on each of four bands with equal logarithmic width. Consonant identification was tested in backgrounds of continuous and various types of fluctuating speech-shaped Gaussian noise including those with both regularly and irregularly spaced temporal fluctuations. Listeners with HI achieved similar scores for EEQ and the original (unprocessed) stimuli in continuous-noise backgrounds, while superior performance was obtained for the EEQ signals in fluctuating background noises that had regular temporal gaps but not for those with irregularly spaced fluctuations. Thus, in noise backgrounds with regularly spaced temporal fluctuations, the energy-normalized signals led to larger values of MR and higher intelligibility than obtained with unprocessed signals.
对于听力正常(NH)的听众来说,掩蔽释放(MR;即在波动噪声背景下比在连续噪声背景下更好的语音识别)是显而易见的,但对于感音神经性听力障碍(HI)的听众来说,这种掩蔽释放通常会降低或消失。在这项研究中,开发了一种实时信号处理技术,以改善 HI 听众的 MR,并深入了解影响 MR 大小的机制。该技术比较了能量的短期和长期估计,增加了能量低于平均能量的短期段的水平,并将处理后的信号的整体能量归一化为原始长期估计的能量。该信号处理算法用于创建两种类型的能量均衡(EEQ)信号:EEQ1,作用于宽带语音加噪声信号;EEQ4,独立作用于具有相等对数宽度的四个频段的每一个。在连续噪声背景和各种类型的波动语音高斯噪声背景下,包括具有规则和不规则时间波动的噪声背景下,测试了辅音识别。在连续噪声背景下,HI 患者对 EEQ 和原始(未处理)刺激的得分相似,而在具有规则时间间隙的波动背景噪声中,EEQ 信号的性能更好,但在具有不规则时间波动的背景噪声中则不然。因此,在具有规则时间波动的噪声背景下,与未处理的信号相比,能量归一化的信号导致更大的 MR 值和更高的可懂度。