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Transcript
WS4 Improving speech perception with cochlear implants using model-based
approaches
WS4-1
Towards a model based coding strategy for cochlear implants using spectral contrast enhancement
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Nogueira Vazquez W. , Rode T. , Penninger R. , Kludt E. , Büchner A.
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Hannover Medical School, German Hearing Center, Hannover, Germany
Introduction: Considerable variation in speech intelligibility outcomes when comparing two sound coding
strategies has been seen in many clinical studies, even if the study participants were postlingually hearing
impaired adults and had at least 2 years of experience with their cochlear implant. One possible reason that
might explain this variability is the electrode nerve interface of each individual which has an impact on the
spectral resolution of a Cochlear Implant. Spectral resolution has been reported to be closely related to vowel
and consonant recognition in CI listeners (1). One measure of spectral resolution is the spectral modulation
threshold (SMT), which is defined as the smallest detectable spectral contrast in the spectral ripple stimulus (1).
Methods: In this study we hypothesized that an algorithm which is able to improve SMT might also be able to
improve vowel recognition, and consequently produce an improvement in speech understanding. For this
purpose we implemented an algorithm termed Spectral Contrast Enhancement (SCE) that is able to emphasize
peaks with respect to valleys in the audio spectrum (2;3). This algorithm can be configured with a single
parameter, the Spectral Contrast Enhancement (SCE) factor. Additionally, we investigated whether the “SCE
factor” can be individualized for each CI user to maximize their vowel identification scores.
For this purpose we developed a peripheral model of the neural activity evoked by CI stimulation. The model has
been individualized to the electrode nerve characteristics of each study participant, for example using information
about their cochlear size, electrode position and impedance measurements. Next, the parameters of the model
were adjusted using a pattern recognition algorithm to match the SMT of each subject. Finally, the model was
used to predict the performance produced by the SCE algorithm with two different “SCE factors” in a vowel
identification task.
Results: In 7 CI users the new algorithm has been evaluated using a SMT task and a vowel identification task in
noise. Audio signals were processed with and without the SCE algorithm and presented to the CI users through
the nucleus research interface at an equivalent level of 65 dB SPL. The task was performed for SCE factors of 1
(no enhancement), 3 and 5.
6 out of 7 CI users obtained an improvement in the SMT task corresponding to their improvement in vowel
identification scores with an SCE factor of either 3 or 5. The mean improvements obtained by the SCE algorithm
for the SMT and the vowel/consonant identification task were 1.9 dB and 5% respectively. The individualized
cochlear implant model was able to predict the optimal “SCE factor” for all study participants.
References:
1. Litvak LM, Spahr AJ, Saoji AA et al. Relationship between perception of spectral ripple and speech
recognition in cochlear implant and vocoder listeners. J Acoust Soc Am 2007;122:982-991.
2. Loizou PC, Poroy O. Minimum spectral contrast needed for vowel identification by normal hearing and
cochlear implant listeners. J Acoust Soc Am 2001;110:1619-1627.
3. Bhattacharya A, Zeng FG. Companding to improve cochlear-implant speech recognition in speechshaped noise. J Acoust Soc Am 2007;122:1079-1089.
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