High levels of speech recognition have been obtained with cochlear implant users in quiet conditions. In noisy environments, speech recognition deteriorates considerably, especially in speech-like noise. The aim of this study was to determine what underlies measured speech recognition in cochlear implantees, and furthermore, what underlies perception of speech in noise. Vowel and consonant recognition was determined in ten normal-hearing listeners using acoustic simulations. An acoustic model was developed in order to process vowels and consonants in quiet and noisy conditions; multi-talker babble and speech-like noise were added to the speech segments for the noisy conditions. A total of seven conditions were simulated acoustically; namely for recognition in quiet and as a function of signal-to-noise ratio (0 dB, 20 dB and 40 dB speech-like noise and 0 dB, 20 dB and 40 dB multi-talker babble). An eight- channel SPEAK processor was modelled and used to process the speech segments. A number of biophysical interactions between simulated nerve fibres and the cochlear implant were simulated by including models of these interactions in the acoustic model. Biophysical characteristics that were modelled included dynamic range compression and current spread in the cochlea. Recognition scores deteriorated with increasing noise levels, as expected. Vowel recognition was better than consonant recognition in general. In quiet conditions, the features transmitted most efficiently for recognition of speech segments were duration and F2 for vowels and burst and affrication for consonants. In noisy conditions, listeners mainly depended on the duration of vowels for recognition and the burst of consonants. As the SNR decreased, the number of features used to recognise speech segments also became fewer. This suggests that the addition of noise reduces the number of acoustic features available for recognition. Efforts to improve the transmission of important speech features m cochlear implants should improve recognition of speech in noisy conditions.
Dissertation (MEng (Bio-Engineering))--University of Pretoria, 2008.