Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1299-1309 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 49 |
| Issue number | 11 |
| DOIs | |
| State | Published - Nov 1 2002 |
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Keywords
- Bionic wavelet transform
- Cochlear implants
- Neural networks
- Speech signal processing
ASJC Scopus subject areas
- Biomedical Engineering
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The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations. / Yao, Jun; Zhang, Yuan Ting.
In: IEEE Transactions on Biomedical Engineering, Vol. 49, No. 11, 01.11.2002, p. 1299-1309.Research output: Contribution to journal › Article
TY - JOUR
T1 - The application of bionic wavelet transform to speech signal processing in cochlear implants using neural network simulations
AU - Yao, Jun
AU - Zhang, Yuan Ting
PY - 2002/11/1
Y1 - 2002/11/1
N2 - 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.
AB - 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.
KW - Bionic wavelet transform
KW - Cochlear implants
KW - Neural networks
KW - Speech signal processing
UR - http://www.scopus.com/inward/record.url?scp=0036845116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036845116&partnerID=8YFLogxK
U2 - 10.1109/TBME.2002.804590
DO - 10.1109/TBME.2002.804590
M3 - Article
C2 - 12450360
AN - SCOPUS:0036845116
VL - 49
SP - 1299
EP - 1309
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 11
ER -