Talker Adaptation and Lexical Difficulty Impact Word Recognition in Adults with Cochlear Implants

<b><i>Introduction:</i></b> Talker-specific adaptation facilitates speech recognition in normal-hearing listeners. This study examined talker adaptation in adult cochlear implant (CI) users. Three hypotheses were tested: (1) high-performing adult CI users show improved word recognition following exposure to a talker (“talker adaptation”), particularly for lexically hard words, (2) individual performance is determined by auditory sensitivity and neurocognitive skills, and (3) individual performance relates to real-world functioning. <b><i>Methods:</i></b> Fifteen high-performing, post-lingually deaf adult CI users completed a word recognition task consisting of 6 single-talker blocks (3 female/3 male native English speakers); words were lexically “easy” and “hard.” Recognition accuracy was assessed “early” and “late” (first vs. last 10 trials); adaptation was assessed as the difference between late and early accuracy. Participants also completed measures of spectral-temporal processing and neurocognitive skills, as well as real-world measures of multiple-talker sentence recognition and quality of life (QoL). <b><i>Results:</i></b> CI users showed limited talker adaptation overall, but performance improved for lexically hard words. Stronger spectral-temporal processing and neurocognitive skills were weakly to moderately associated with more accurate word recognition and greater talker adaptation for hard words. Finally, word recognition accuracy for hard words was moderately related to multiple-talker sentence recognition and QoL. <b><i>Conclusion:</i></b> Findings demonstrate a limited talker adaptation benefit for recognition of hard words in adult CI users. Both auditory sensitivity and neurocognitive skills contribute to performance, suggesting additional benefit from adaptation for individuals with stronger skills. Finally, processing differences related to talker adaptation and lexical difficulty may be relevant to real-world functioning.

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution

Visual-auditory sensory substitution has demonstrated great potential to help visually impaired and blind groups to recognize objects and to perform basic navigational tasks. However, the high latency between visual information acquisition and auditory transduction may contribute to the lack of the successful adoption of such aid technologies in the blind community; thus far, substitution methods have remained only laboratory-scale research or pilot demonstrations. This high latency for data conversion leads to challenges in perceiving fast-moving objects or rapid environmental changes. To reduce this latency, prior analysis of auditory sensitivity is necessary. However, existing auditory sensitivity analyses are subjective because they were conducted using human behavioral analysis. Therefore, in this study, we propose a cross-modal generative adversarial network-based evaluation method to find an optimal auditory sensitivity to reduce transmission latency in visual-auditory sensory substitution, which is related to the perception of visual information. We further conducted a human-based assessment to evaluate the effectiveness of the proposed model-based analysis in human behavioral experiments. We conducted experiments with three participant groups, including sighted users (SU), congenitally blind (CB) and late-blind (LB) individuals. Experimental results from the proposed model showed that the temporal length of the auditory signal for sensory substitution could be reduced by 50%. This result indicates the possibility of improving the performance of the conventional vOICe method by up to two times. We confirmed that our experimental results are consistent with human assessment through behavioral experiments. Analyzing auditory sensitivity with deep learning models has the potential to improve the efficiency of sensory substitution.