CompHEAR: A Customizable and Scalable Web-Enabled Auditory Performance Evaluation Platform for Cochlear Implant Sound Processing Research.

Objective: Cochlear implants (CIs) are auditory prostheses for individuals with severe to profound hearing loss, offering substantial but incomplete restoration of hearing function by stimulating the auditory nerve using electrodes. However, progress in CI performance and innovation has been constrained by the inability to rapidly test multiple sound processing strategies. Current research interfaces provided by major CI manufacturers have limitations in supporting a wide range of auditory experiments due to portability, programming difficulties, and the lack of direct comparison between sound processing algorithms. To address these limitations, we present the CompHEAR research platform, designed specifically for the Cochlear Implant Hackathon, enabling researchers to conduct diverse auditory experiments on a large scale. Study Design: Quasi-experimental. Setting: Virtual. Methods: CompHEAR is an open-source, user-friendly platform which offers flexibility and ease of customization, allowing researchers to set up a broad set of auditory experiments. CompHEAR employs a vocoder to simulate novel sound coding strategies for CIs. It facilitates even distribution of listening tasks among participants and delivers real-time metrics for evaluation. The software architecture underlies the platform's flexibility in experimental design and its wide range of applications in sound processing research. Results: Performance testing of the CompHEAR platform ensured that it could support at least 10,000 concurrent users. The CompHEAR platform was successfully implemented during the COVID-19 pandemic and enabled global collaboration for the CI Hackathon (www.cihackathon.com). Conclusion: The CompHEAR platform is a useful research tool that permits comparing diverse signal processing strategies across a variety of auditory tasks with crowdsourced judging. Its versatility, scalability, and ease of use can enable further research with the goal of promoting advancements in cochlear implant performance and improved patient outcomes.

Development and Validation of the Spanish AzBio Sentence Corpus.

OBJECTIVE: To create and validate a Spanish sentence test for evaluation of speech understanding of Spanish-speaking listeners with hearing loss or cochlear implants (CI). STUDY DESIGN: Two thousand sentences were recorded from two male and two female speakers. The average intelligibility of each sentence was estimated as the mean score achieved by five listeners presented with a five-channel cochlear implant simulation. The mean scores of each sentence were used to construct 42 lists of 20 sentences with similar mean scores. List equivalency was then validated by presenting all lists to 10 CI users and in a 2-list comparison in a clinical setting to 38 CI patients. SETTING: Tertiary referral center. PATIENTS: Normal-hearing listeners (n = 5), CI users in a research study (n = 10), and CI patients (n = 38) in routine clinical follow-up. INTERVENTION: Multiple sentence lists from a newly minted speech perception test. MAIN OUTCOME MEASURES: List intelligibility and equivalence across sentence lists. RESULTS: Forty-two lists of sentences were equivalent when all lists were presented in random order to 10 adult CI recipients. The variability of scores observed on lists presented to the same listener in the same condition was captured using a binomial distribution model based on a 40-item list for 38 adult implant recipients. CONCLUSION: The Spanish AzBio Sentence Test includes 42 lists of 20 sentences. These sentences are roughly equivalent in terms of overall difficulty and confidence limits have been provided to assess the significance of variability in list scores observed within or across conditions. These materials will be of benefit when assessing native Spanish speakers in both research and clinical settings.

Evaluation of a New Algorithm to Optimize Audibility in Cochlear Implant Recipients.

OBJECTIVES: A positive relation between audibility and speech understanding has been established for cochlear implant (CI) recipients. Sound field thresholds of 20 dB HL across the frequency range provide CI users the opportunity to understand soft and very soft speech. However, programming the sound processor to attain good audibility can be time-consuming and difficult for some patients. To address these issues, Advanced Bionics (AB) developed the SoftVoice algorithm designed to remove system noise and thereby improve audibility of soft speech. The present study aimed to evaluate the efficacy of SoftVoice in optimizing AB CI recipients' soft-speech perception. DESIGN: Two studies were conducted. Study 1 had two phases, 1A and 1B. Sixteen adult, AB CI recipients participated in Study 1A. Acute testing was performed in the unilateral CI condition using a Harmony processor programmed with participants' everyday-use program (Everyday) and that same program but with SoftVoice implemented. Speech recognition measures were administered at several presentation levels in quiet (35 to 60 dB SPL) and in noise (60 dB SPL). In Study 1B, 10 of the participants compared Everyday and SoftVoice at home to obtain feedback regarding the use of SoftVoice in various environments. During Study 2, soft-speech perception was acutely measured with Everyday and SoftVoice for 10 participants using the Naida CI Q70 processor. Results with the Harmony (Study 1A) and Naida processors were compared. Additionally, Study 2 evaluated programming options for setting electrode threshold levels (T-levels or Ts) to improve the usability of SoftVoice in daily life. RESULTS: Study 1A showed significantly higher scores with SoftVoice than Everyday at soft presentation levels (35, 40, 45, and 50 dB SPL) and no significant differences between programs at a conversational level (60 dB SPL) in quiet or in noise. After take-home experience with SoftVoice and Everyday (Study 1B), 5 of 10 participants reported preferring SoftVoice over Everyday; however, 6 reported bothersome environmental sound when listening with SoftVoice at home. Results of Study 2 indicated similar soft-speech perception between Harmony and Naida processors. Additionally, implementing SoftVoice with Ts at the manufacturer's default setting of 10% of Ms reduced reports of bothersome environmental sound during take-home experience; however, soft-speech perception was best with SoftVoice when Ts were behaviorally set above 10% of Ms. CONCLUSIONS: Results indicate that SoftVoice may be a potential tool for optimizing AB users' audibility and, in turn, soft-speech perception. To achieve optimal performance at soft levels and comfortable use in daily environments, setting Ts must be considered with SoftVoice. Future research should examine program parameters that may benefit soft-speech perception when used in combination with SoftVoice (e.g., increased input dynamic range).