Faculty Bibliography

OBJECTIVES/OBJECTIVE:This study aims to validate vocoders as acoustic models of cochlear implants by determining whether they capture both perceptual sound quality and speech performance. We hypothesize that valid acoustic models of cochlear implants require listener-specific amounts of frequency mismatch between input filters and output tones or noise bands. DESIGN/METHODS:Forty-four adult single-sided deaf cochlear implant users were tested 1 to 5 times for a total of 73 sessions at different time points after initial stimulation. Participants had a cochlear implant in one ear and normal or near-normal hearing in the contralateral ear, allowing within-subject comparisons. In Experiment 1, participants used a method-of-adjustment procedure to select acoustic models most similar to their cochlear implant by adjusting three parameters: low- and high-frequency cutoffs of the acoustic output, and channel interaction (overlap among output sound carriers). In Experiment 2, participants rated the perceptual similarity of five acoustic model types using questionnaires assessing overall similarity and four acoustic dimensions (intelligibility, pleasantness, harshness, and loudness). The five model types included the self-selected model, two all-channel frequency-matched vocoders (tone and noise), and two six-channel frequency-matched vocoders (tone and noise). Frequency-matched acoustic models had output noise bands or tones that were frequency-matched to the analysis filters. In Experiment 3, speech perception was evaluated under six conditions: with the cochlear implant alone and with each of the five acoustic model types presented to the normal hearing ear. RESULTS:Nearly all participants (69 of 73 sessions) selected acoustic models with frequency ranges different from their clinical frequency allocation tables. The low-frequency edge of self-selected models was significantly higher than clinical allocations (456 Hz for Cochlear Ltd. and 266 Hz for MED-EL). Over 80% of selections used minimal channel interaction (tones, nonoverlapping noise bands, or adjacent noise bands). Self-selected models received significantly higher similarity ratings (mean of 6.11, where 6 means "somewhat similar" and 7 "very similar") compared with all frequency-matched models, which were rated around 3 ("not very similar"). Self-selected models were rated most similar to the cochlear implant across multiple acoustic dimensions and were the only models not rated significantly different from the cochlear implant on any dimension. For speech perception, all-channel frequency-matched models significantly overestimated performance compared with the cochlear implant for both words and sentences. Self-selected models provided speech scores closest to cochlear implant performance. Joint analysis of similarity ratings and speech perception scores demonstrated that self-selected models were the only acoustic models achieving both perceptual similarity to the cochlear implant (rating of 6.11 with 7 being "very similar") and comparable speech perception scores (within five percentage points). CONCLUSIONS:Frequency-matched acoustic models fail to replicate the sound of a cochlear implant and all-channel frequency-matched acoustic models also significantly overestimate speech perception. In contrast, self-selected acoustic models incorporating listener-specific perceptual frequency mismatch provide substantially better matches in both subjective sound quality and speech intelligibility. However, variability in similarity ratings across individuals may suggest that additional perceptual components remain unaccounted for in the current parameter set. These findings question the validity of frequency-matched acoustic models of cochlear implants in research applications.

OBJECTIVES/OBJECTIVE:To provide a level-adjusted correction to the current standard relating anatomical cochlear place to characteristic frequency (CF) in humans, and to re-evaluate anatomical frequency mismatch in cochlear implant (CI recipients considering this correction. It is proposed that a level-adjusted place-frequency function may represent a more relevant tonotopic benchmark for CIs in comparison to the current standard. DESIGN/METHODS:The present analytical study compiled data from 15 previous animal studies that reported isointensity responses from cochlear structures at different stimulation levels. Extracted outcome measures were CFs and centroid-based best frequencies at 70 dB SPL input from 47 specimens spanning a broad range of cochlear locations. A simple relationship was used to transform these measures to human estimates of characteristic and best frequencies, and nonlinear regression was applied to these estimates to determine how the standard human place-frequency function should be adjusted to reflect best frequency rather than CF. The proposed level-adjusted correction was then compared with average place-frequency positions of commonly used CI devices when programmed with clinical settings. RESULTS:The present study showed that the best frequency at 70 dB SPL (BF70) tends to shift away from CF. The amount of shift was statistically significant (signed-rank test z = 5.143, p < 0.001), but the amount and direction of shift depended on cochlear location. At cochlear locations up to 600° from the base, BF70 shifted downward in frequency relative to CF by about 4 semitones on average. Beyond 600° from the base, BF70 shifted upward in frequency relative to CF by about 6 semitones on average. In terms of spread (90% prediction interval), the amount of shift between CF and BF70 varied from relatively no shift to nearly an octave of shift. With the new level-adjusted place-frequency function, the amount of anatomical frequency mismatch for devices programmed with standard-of-care settings is less extreme than originally thought and may be nonexistent for all but the most apical electrodes. CONCLUSIONS:The present study validates the current standard for relating cochlear place to CF, and introduces a level-adjusted correction for how best frequency shifts away from CF at moderately loud stimulation levels. This correction may represent a more relevant tonotopic reference for CIs. To the extent that it does, its implementation may potentially enhance perceptual accommodation and speech understanding in CI users, thereby improving CI outcomes and contributing to advancements in the programming and clinical management of CIs.

HYPOTHESIS/OBJECTIVE:This study tests the hypothesis that it is possible to find tone or noise vocoders that sound similar and result in similar speech perception scores to a cochlear implant (CI). This would validate the use of such vocoders as acoustic models of CIs. We further hypothesize that those valid acoustic models will require a personalized amount of frequency mismatch between input filters and output tones or noise bands. BACKGROUND:Noise or tone vocoders have been used as acoustic models of CIs in hundreds of publications but have never been convincingly validated. METHODS:Acoustic models were evaluated by single-sided deaf CI users who compared what they heard with the CI in one ear to what they heard with the acoustic model in the other ear. We evaluated frequency-matched models (both all-channel and 6-channel models, both tone and noise vocoders) as well as self-selected models that included an individualized level of frequency mismatch. RESULTS:Self-selected acoustic models resulted in similar levels of speech perception and similar perceptual quality as the CI. These models also matched the CI in terms of perceived intelligibility, harshness, and pleasantness. CONCLUSION/CONCLUSIONS:Valid acoustic models of CIs exist, but they are different from the models most widely used in the literature. Individual amounts of frequency mismatch may be required to optimize the validity of the model. This may be related to the basalward frequency mismatch experienced by postlingually deaf patients after cochlear implantation.

Binaural unmasking, a key feature of normal binaural hearing, can refer to the improved intelligibility of masked speech by adding masking that facilitates perceived separation of target and masker. A question relevant for cochlear implant users with single-sided deafness (SSD-CI) is whether binaural unmasking can still be achieved if the additional masking is spectrally degraded and shifted. CIs restore some aspects of binaural hearing to these listeners, although binaural unmasking remains limited. Notably, these listeners may experience a mismatch between the frequency information perceived through the CI and that perceived by their normal hearing ear. Employing acoustic simulations of SSD-CI with normal hearing listeners, the present study confirms a previous simulation study that binaural unmasking is severely limited when interaural frequency mismatch between the input frequency range and simulated place of stimulation exceeds 1-2 mm. The present study also shows that binaural unmasking is largely retained when the input frequency range is adjusted to match simulated place of stimulation, even at the expense of removing low-frequency information. This result bears implications for the mechanisms driving the type of binaural unmasking of the present study and for mapping the frequency range of the CI speech processor in SSD-CI users.

A potential bottleneck to improving speech perception performance in cochlear implant (CI) users is that some of their electrodes may poorly encode speech information. Several studies have examined the effect of deactivating poorly encoding electrodes on speech perception with mixed results. Many of these studies focused on identifying poorly encoding electrodes by some measure (e.g. electrode discrimination, pitch ordering, threshold, CT-guided, masked modulation detection), but provide inconsistent criteria about which electrodes, and how many, should be deactivated, and without considering how speech information becomes distributed across the electrode array. The present simulation study addresses this issue using computational approaches. Previously validated models were used to generate predictions of speech scores as a function of all possible combinations of active electrodes in a 22-electrode array in three groups of hypothetical subjects representative of relatively better, moderate, and poorer performing CI users. Using high-performance computing, over 500 million predictions were generated. Although deactivation of the poorest encoding electrodes sometimes resulted in predicted benefit, this benefit was significantly less relative to predictions resulting from model-optimized deactivations. This trend persisted when using novel stimuli (i.e. other than those used for optimization) and when using different processing strategies. Optimum electrode deactivation patterns produced an average predicted increase in word scores of 10% with some scores increasing by more than 20%. Optimum electrode deactivation patterns typically included 11 to 19 (out of 22) active electrodes, depending on the performance group. Optimal active electrode combinations were those that maximized discrimination of speech cues, maintaining 80%-100% of the physical span of the array. The present study demonstrates the potential for further improving CI users' speech scores with appropriate selection of active electrodes.

HYPOTHESIS: A novel smartphone-based software application can facilitate self-selection of frequency allocation tables (FAT) in postlingually deaf cochlear implant (CI) users. BACKGROUND: CIs use FATs to represent the tonotopic organization of a normal cochlea. Current CI fitting methods typically use a standard FAT for all patients regardless of individual differences in cochlear size and electrode location. In postlingually deaf patients, different amounts of mismatch can result between the frequency-place function they experienced when they had normal hearing and the frequency-place function that results from the standard FAT. For some CI users, an alternative FAT may enhance sound quality or speech perception. Currently, no widely available tools exist to aid real-time selection of different FATs. This study aims to develop a new smartphone tool for this purpose and to evaluate speech perception and sound quality measures in a pilot study of CI subjects using this application. METHODS: A smartphone application for a widely available mobile platform (iOS) was developed to serve as a preprocessor of auditory input to a clinical CI speech processor and enable interactive real-time selection of FATs. The application's output was validated by measuring electrodograms for various inputs. A pilot study was conducted in six CI subjects. Speech perception was evaluated using word recognition tests. RESULTS: All subjects successfully used the portable application with their clinical speech processors to experience different FATs while listening to running speech. The users were all able to select one table that they judged provided the best sound quality. All subjects chose a FAT different from the standard FAT in their everyday clinical processor. Using the smartphone application, the mean consonant-nucleus-consonant score with the default FAT selection was 28.5% (SD 16.8) and 29.5% (SD 16.4) when using a self-selected FAT. CONCLUSION: A portable smartphone application enables CI users to self-select frequency allocation tables in real time. Even though the self-selected FATs that were deemed to have better sound quality were only tested acutely (i.e., without long-term experience with them), speech perception scores were not inferior to those obtained with the clinical FATs. This software application may be a valuable tool for improving future methods of CI fitting.

Cochlear implant (CI) recipients have difficulty understanding speech in noise even at moderate signal-to-noise ratios. Knowing the mechanisms they use to understand speech in noise may facilitate the search for better speech processing algorithms. In the present study, a computational model is used to assess whether CI users' vowel identification in noise can be explained by formant frequency cues (F1 and F2). Vowel identification was tested with 12 unilateral CI users in quiet and in noise. Formant cues were measured from vowels in each condition, specific to each subject's speech processor. Noise distorted the location of vowels in the F2 vs F1 plane in comparison to quiet. The best fit model to subjects' data in quiet produced model predictions in noise that were within 8% of actual scores on average. Predictions in noise were much better when assuming that subjects used a priori knowledge regarding how formant information is degraded in noise (experiment 1). However, the model's best fit to subjects' confusion matrices in noise was worse than in quiet, suggesting that CI users utilize formant cues to identify vowels in noise, but to a different extent than how they identify vowels in quiet (experiment 2).

Even though speech signals trigger coding in the cochlea to convey speech information to the central auditory structures, little is known about the neural mechanisms involved in such processes. The purpose of this study was to understand the encoding of formant cues and how it relates to vowel recognition in listeners. Neural representations of formants may differ across listeners; however, it was hypothesized that neural patterns could still predict vowel recognition. To test the hypothesis, the frequency-following response (FFR) and vowel recognition were obtained from 38 normal-hearing listeners using four different vowels, allowing direct comparisons between behavioral and neural data in the same individuals. FFR was employed because it provides an objective and physiological measure of neural activity that can reflect formant encoding. A mathematical model was used to describe vowel confusion patterns based on the neural responses to vowel formant cues. The major findings were (1) there were large variations in the accuracy of vowel formant encoding across listeners as indexed by the FFR, (2) these variations were systematically related to vowel recognition performance, and (3) the mathematical model of vowel identification was successful in predicting good vs poor vowel identification performers based exclusively on physiological data.

Abstract Conclusion: The human frequency-to-place map may be modified by experience, even in adult listeners. However, such plasticity has limitations. Knowledge of the extent and the limitations of human auditory plasticity can help optimize parameter settings in users of auditory prostheses. Objectives: To what extent can adults adapt to sharply different frequency-to-place maps across ears? This question was investigated in two bilateral cochlear implant users who had a full electrode insertion in one ear, a much shallower insertion in the other ear, and standard frequency-to-electrode maps in both ears. Methods: Three methods were used to assess adaptation to the frequency-to-electrode maps in each ear: (1) pitch matching of electrodes in opposite ears, (2) listener-driven selection of the most intelligible frequency-to-electrode map, and (3) speech perception tests. Based on these measurements, one subject was fitted with an alternative frequency-to-electrode map, which sought to compensate for her incomplete adaptation to the standard frequency-to-electrode map. Results: Both listeners showed remarkable ability to adapt, but such adaptation remained incomplete for the ear with the shallower electrode insertion, even after extended experience. The alternative frequency-to-electrode map that was tested resulted in substantial increases in speech perception for one subject in the short insertion ear.