The effects of reverberant self- and overlap-masking on speech recognition in cochlear implant listeners.

Many cochlear implant (CI) listeners experience decreased speech recognition in reverberant environments [Kokkinakis et al., J. Acoust. Soc. Am. 129(5), 3221-3232 (2011)], which may be caused by a combination of self- and overlap-masking [Bolt and MacDonald, J. Acoust. Soc. Am. 21(6), 577-580 (1949)]. Determining the extent to which these effects decrease speech recognition for CI listeners may influence reverberation mitigation algorithms. This study compared speech recognition with ideal self-masking mitigation, with ideal overlap-masking mitigation, and with no mitigation. Under these conditions, mitigating either self- or overlap-masking resulted in significant improvements in speech recognition for both normal hearing subjects utilizing an acoustic model and for CI listeners using their own devices.

Using channel-specific statistical models to detect reverberation in cochlear implant stimuli.

Reverberation is especially detrimental for cochlear implant listeners; thus, mitigating its effects has the potential to provide significant improvements to cochlear implant communication. Efforts to model and correct for reverberation in acoustic listening scenarios can be quite complex, requiring estimation of the room transfer function and localization of the source and receiver. However, due to the limited resolution associated with cochlear implant stimulation, simpler processing for reverberation detection and mitigation may be possible for cochlear implants. This study models speech stimuli in a cochlear implant on a per-channel basis both in quiet and in reverberation, and assesses the efficacy of these models for detecting the presence of reverberation. This study was able to successfully detect reverberation in cochlear implant pulse trains, and the results appear to be robust to varying room conditions and cochlear implant stimulation parameters. Reverberant signals were detected 100% of the time for a long reverberation time of 1.2 s and 86% of the time for a shorter reverberation time of 0.5 s.

Stream segregation on a single electrode as a function of pulse rate in cochlear implant listeners.

While cochlear implants (CIs) usually provide high levels of speech recognition in quiet, speech recognition in noise remains challenging. To overcome these difficulties, it is important to understand how implanted listeners separate a target signal from interferers. Stream segregation has been studied extensively in both normal and electric hearing, as a function of place of stimulation. However, the effects of pulse rate, independent of place, on the perceptual grouping of sequential sounds in electric hearing have not yet been investigated. A rhythm detection task was used to measure stream segregation. The results of this study suggest that while CI listeners can segregate streams based on differences in pulse rate alone, the amount of stream segregation observed decreases as the base pulse rate increases. Further investigation of the perceptual dimensions encoded by the pulse rate and the effect of sequential presentation of different stimulation rates on perception could be beneficial for the future development of speech processing strategies for CIs.

Investigating the effects of stimulus duration and context on pitch perception by cochlear implant users.

Cochlear implant sound processing strategies that use time-varying pulse rates to transmit fine structure information are one proposed method for improving the spectral representation of a sound with the eventual goal of improving speech recognition in noisy conditions, speech recognition in tonal languages, and music identification and appreciation. However, many of the perceptual phenomena associated with time-varying rates are not well understood. In this study, the effects of stimulus duration on both the place and rate-pitch percepts were investigated via psychophysical experiments. Four Nucleus CI24 cochlear implant users participated in these experiments, which included a short-duration pitch ranking task and three adaptive pulse rate discrimination tasks. When duration was fixed from trial-to-trial and rate was varied adaptively, results suggested that both the place-pitch and rate-pitch percepts may be independent of duration for durations above 10 and 20 ms, respectively. When duration was varied and pulse rates were fixed, performance was highly variable within and across subjects. Implications for multi-rate sound processing strategies are discussed.

Assessing the pitch structure associated with multiple rates and places for cochlear implant users.

Cochlear implant subjects continue to experience difficulty understanding speech in noise and performing pitch-based musical tasks. Acoustic model studies have suggested that transmitting additional fine structure via multiple stimulation rates is a potential mechanism for addressing these issues [Nie et al., IEEE Trans. Biomed. Eng. 52, 64-73 (2005); Throckmorton et al., Hear. Res. 218, 30-42 (2006)]; however, results from preliminary cochlear implant studies have been less compelling. Multirate speech processing algorithms previously assumed a place-dependent pitch structure in that a basal electrode would always elicit a higher pitch percept than an apical electrode, independent of stimulation rate. Some subjective evidence contradicts this assumption [H. J. McDermott and C. M. McKay, J. Acoust. Soc. Am. 101, 1622-1630 (1997); R. V. Shannon, Hear. Res. 11, 157-189 (1983)]. The purpose of this study is to test the hypothesis that the introduction of multiple rates may invalidate the tonotopic pitch structure resulting from place-pitch alone. The SPEAR3 developmental speech processor was used to collect psychophysical data from five cochlear implant users to assess the tonotopic structure for stimuli presented at two rates on all active electrodes. Pitch ranking data indicated many cases where pitch percepts overlapped across electrodes and rates. Thus, the results from this study suggest that pitch-based tuning across rate and electrode may be necessary to optimize performance of a multirate sound processing strategy in cochlear implant subjects.

Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, Nucl. However, no specific rule for determining Nucl has been suggested. Our work determines the uncomfortable level based on the excitation pattern of the neural response in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven by a stimulus at an uncomfortable level in a normal ear is related to Nucl at an uncomfortable level of the electrical stimulus. Intensity discrimination limens are predicted using signal detection theory via the probability mass function of the neural response and via experimental simulations. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly.

Predicting auditory tone-in-noise detection performance: the effects of neural variability.

Collecting and analyzing psychophysical data is a fundamental mechanism for the study of auditory processing. However, because this approach relies on human listening experiments, it can be costly in terms of time and money spent gathering the data. The development of a theoretical, model-based procedure capable of accurately predicting psychophysical behavior could alleviate these issues by enabling researchers to rapidly evaluate hypotheses prior to conducting experiments. This approach may also provide additional insight into auditory processing by establishing a link between psychophysical behavior and physiology. Signal detection theory has previously been combined with an auditory model to generate theoretical predictions of psychophysical behavior. Commonly, the ideal processor outperforms human subjects. In order for this model-based technique to enhance the study of auditory processing, discrepancies must be eliminated or explained. In this paper, we investigate the possibility that neural variability, which results from the randomness inherent in auditory nerve fiber responses, may explain some of the previously observed discrepancies. In addition, we study the impact of combining information across nerve fibers and investigate several models of multiple-fiber signal processing. Our findings suggest that neural variability can account for much, but not all, of the discrepancy between theoretical and experimental data.