Manchaiah, V.K.C.
Thesis towards ‘M.Sc Audiology’, Institute of Sound and Vibration Research, University of Southampton, Southampton, UK.
Publication year: 2007


Aim: The aim of the present study is to investigate the effect of high electrical stimulation rates of Cochlear Implants (CI) on speech perception.

Method: Four Nucleus Freedom CI users and five normal hearing subjects performed VCV consonant (iCi) recognition in quiet and BKB sentence recognition in presence of background noise at +10 dB SNR. Three stimulation rates of 1800 pps/ch, 2400 pps/ch & 3500 pps/ch were used with ACE (RE) speech coding strategy and the performance of subject’s measured in all three rate conditions.  All testing was conducted in a sound treated room with noise levels less than 30 dB (A). The 22 channel ACE (RE) speech coding strategy was implemented through the custom interface of the Nucleus Implant Communicator (NIC-Stream) simulation software (by Cochlear) and AMO software (Laneau, Moonen & Wouters; 2006) with MATLAB software to generate the CI acoustic models for normal hearing subjects. Normal hearing subjects with CI acoustic modelling were also studied to see the effect of overlap on CI acoustic modelling.

Analysis: The speech perception results from adult patients using the 4th generation CI such as the Nucleus Freedom system were compared with the results of normal hearing subjects with CI acoustic modelling. VCV test score were also further analysed for feature transmission errors in place of articulation, manner of articulation and voicing features. Repeated measures ANOVA were used to study the interaction of stimulation rates on speech perception.

Results & Conclusion: There was no significant difference in score of consonant recognition in quiet and sentence recognition in noise test among different stimulation rate for both groups. The CI acoustic models were found to be useful in studying the effect of rates in CI users. The overlap leads to poor performance in consonant recognition in quiet and sentence recognition in noise test. However, the CI acoustic models with overlap seemed to be the better approximation of CI user’s scores. There was no advantage of high stimulation rates seen for both consonant recognition in quiet and sentence recognition in noise.