Yao Jun, Zhang Yuan-Ting
Department of Electronic Engineering, Chinese University of Hong Kong, N. T., Shatin, Hong Kong.
IEEE Trans Biomed Eng. 2002 Nov;49(11):1299-309. doi: 10.1109/TBME.2002.804590.
Cochlear implants (CIs) restore partial hearing to people with severe to profound sensorineural deafness; but there is still a marked performance gap in speech recognition between those who have received cochlear implant and people with a normal hearing capability. One of the factors that may lead to this performance gap is the inadequate signal processing method used in CIs. This paper investigates the application of an improved signal-processing method called bionic wavelet transform (BWT). This method is based upon the auditory model and allows for signal processing. Comparing the neural network simulations on the same experimental materials processed by wavelet transform (WT) and BWT, the application of BWT to speech signal processing in CI has a number of advantages, including: improvement in recognition rates for both consonants and vowels, reduction of the number of required channels, reduction of the average stimulation duration for words, and high noise tolerance. Consonant recognition results in 15 normal hearing subjects show that the BWT produces significantly better performance than the WT (t = -4.36276, p = 0.00065). The BWT has great potential to reduce the performance gap between CI listeners and people with a normal hearing capability in the future.
人工耳蜗（CIs）能为重度至极重度感音神经性聋患者恢复部分听力；但接受人工耳蜗植入者与听力正常者在言语识别方面仍存在显著的表现差距。可能导致这种表现差距的因素之一是人工耳蜗中使用的信号处理方法不够完善。本文研究了一种名为仿生小波变换（BWT）的改进信号处理方法的应用。该方法基于听觉模型，可进行信号处理。通过对小波变换（WT）和BWT处理的相同实验材料进行神经网络模拟比较，BWT应用于人工耳蜗语音信号处理具有诸多优势，包括：提高辅音和元音的识别率、减少所需通道数量、缩短单词的平均刺激时长以及具有高噪声耐受性。15名听力正常受试者的辅音识别结果表明，BWT的表现明显优于WT（t = -4.36276，p = 0.00065）。未来，BWT在缩小人工耳蜗使用者与听力正常者之间的表现差距方面具有巨大潜力。
