Relationship between perception of spectral ripple and speech recognition in cochlear implant and vocoder listeners

Abstract

Spectral resolution has been reported to be closely related to vowel and consonant recognition in cochlear implant (CI) listeners. One measure of spectral resolution is spectral modulation threshold (SMT), which is defined as the smallest detectable spectral contrast in the spectral ripple stimulus. SMT may be determined by the activation pattern associated with electrical stimulation. In the present study, broad activation patterns were simulated using a multi-band vocoder to determine if similar impairments in speech understanding scores could be produced in normal-hearing listeners. Tokens were first decomposed into 15 logarithmically spaced bands and then re-synthesized by multiplying the envelope of each band by matched filtered noise. Various amounts of current spread were simulated by adjusting the drop-off of the noise spectrum away from the peak (40-5 dBoctave). The average SMT (0.25 and 0.5 cyclesoctave) increased from 6.3 to 22.5 dB, while average vowel identification scores dropped from 86% to 19% and consonant identification scores dropped from 93% to 59%. In each condition, the impairments in speech understanding were generally similar to those found in CI listeners with similar SMTs, suggesting that variability in spread of neural activation largely accounts for the variability in speech perception of CI listeners.

Current focusing and steering: Modeling, physiology, and psychophysics

“…Speech perception of cochlear implant recipients is strongly correlated with their ability to resolve spectral peaks Turner, 2003, 2005;Litvak et al, 2007b;Won et al, 2007). Spectral resolution of cochlear implant listeners may be limited by broad excitation patterns of electric stimulation.…”

“…Goals of current CI research are to identify factors that contribute to this decreased performance and to develop technologies and stimulation strategies that can overcome these deficiencies. For example, speech reception for CI users is related to their ability to perceive spectral shapes (Henry and Turner, 2003;Henry et al, 2005;Litvak et al, 2007b;Won et al, 2007). The ability to discriminate spectral shapes across the electrode array might be limited in part by current spread in the cochlea, and in part by the limited number of distinct stimulation sites in the intracochlear arrays used in contemporary implants and stimulation strategies.…”

Optimization of a Spectral Contrast Enhancement Algorithm for Cochlear Implants Based on a Vowel Identification Model

“…Spectral resolution has been reported to be closely related to vowel and consonant recognition in cochlear implant (CI) listeners (Litvak et al 2007). 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 (Litvak et al 2007). In this study we hypothesize that an SCE algorithm may be able to improve SMT and therefore may also be able to improve vowel recognition.…”

“…One possible reason that might explain this variability is the electrode nerve interface of each individual which can impact the spectral resolution they can achieve. Spectral resolution has been reported to be closely related to vowel and consonant recognition in cochlear implant (CI) listeners (Litvak et al 2007). 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 (Litvak et al 2007).…”

Whispered speech as input for cochlear implants

“…Either the whispered or the phonated member, whichever had the higher mean intensity, was then uniformly attenuated such that the mean intensities of the counterpart utterances were equalized. To simulate speech as Dutch CI-users would perceive it, all stimuli were processed at the ENT department of the Leiden University Medical Centre (LUMC) through an eight-channel noise vocoder (e.g., Litvak et al 2007, Spahr et al 2008) using the CI simulator TIGERCIS (http://www.tigerspeech. com/; last visited 19/05/2010).…”