A model that can accurately predict speech recognition for cochlear implant (CI) listeners is essential for the optimal fitting of cochlear implants. By implementing a CI acoustic model that mimics CI speech processing, the challenge of predicting speech perception in cochlear implants can be simplified. As a first step in predicting the recognition of speech processed through an acoustic model, vowel perception in severe speech-shaped noise was investigated in the current study. The aim was to determine the acoustic cues that listeners use to recognize vowels in severe noise and make suggestions regarding a vowel perception predictor. It is known that formants play an important role in quiet, while in severe noise the role of formants is still unknown. The relative importance of F1 and F2 is also of interest, since the masking of noise is not always evenly distributed over the vowel spectrum. The problem was addressed by synthesizing vowels consisting of either detailed spectral shape or formant information. F1 and F2 were also suppressed to examine the effect in severe noise. The synthetic stimuli were presented to listeners in quiet and signal-to-noise ratios of 0 dB, -5 dB and -10 dB. Results showed that in severe noise, vowels synthesized according to the whole-spectrum were recognized significantly better than vowels containing only formants. Multidimensional scaling and FITA analysis indicated that formants were still perceived and extracted by the human auditory system in severe noise, especially when the vowel spectrum consisted of the whole spectral shape. Although F1 and F2 vary in importance in listening conditions of quiet and less noisy conditions, the role of the two cues appears to be similar in severe noise. It was suggested that not only the availability formants, but also details of the vowel spectral shape can help to predict vowel recognition in severe noise to a certain degree.
Dissertation (MEng)--University of Pretoria, 2013.