The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations

• Published in: IEEE transactions on biomedical engineering Vol. 49; no. 11; pp. 1299 - 1309
• Main Authors: Yao, Jun, Zhang, Yuan-Ting
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Auditory system; Biological and medical sciences; Biomedical signal processing; Cochlear Implants; Computational Intelligence Society; Computer Simulation; Deafness; Humans; Medical sciences; Models, Biological; Models, Statistical; Neural networks; Neural Networks (Computer); Quality Control; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Signal restoration; Speech Acoustics; Speech Intelligibility; Speech Perception; Speech processing; Speech recognition; Transplants & implants; Wavelet transforms; Bionic wavelet transform; cochlear implants; neural networks; speech signal processing
• ISSN: 0018-9294; 1558-2531
• 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.