The impact of noise power estimation on speech intelligibility in cochlear-implant speech coding strategies

Recovering speech intelligibility with deep learning and multiple microphones in noisy-reverberant situations for people using cochlear implants

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep-learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This study compared several deep-learning algorithms with access to one, two unilateral, or six bilateral microphones that were trained to recover speech signals by jointly removing noise and reverberation. The noisy-reverberant speech and an ideal noise reduction algorithm served as lower and upper references, respectively. Objective signal metrics were compared with results from two listening tests, including 15 typical hearing listeners with CI simulations and 12 CI listeners. Large and statistically significant improvements in speech reception thresholds of 7.4 and 10.3 dB were found for the multi-microphone algorithms. For the single-microphone algorithm, there was an improvement of 2.3 dB but only for the CI listener group. The objective signal metrics correctly predicted the rank order of results for CI listeners, and there was an overall agreement for most effects and variances between results for CI simulations and CI listeners. These algorithms hold promise to improve speech intelligibility for CI listeners in environments with noise and reverberation and benefit from a boost in performance when using features extracted from multiple microphones.

Measuring Speech Intelligibility and Hearing-Aid Benefit Using Everyday Conversational Sentences in Real-World Environments

Laboratory and clinical-based assessments of speech intelligibility must evolve to better predict real-world speech intelligibility. One way of approaching this goal is to develop speech intelligibility tasks that are more representative of everyday speech communication outside the laboratory. Here, we evaluate speech intelligibility using both a standard sentence recall task based on clear, read speech (BKB sentences), and a sentence recall task consisting of spontaneously produced speech excised from conversations which took place in realistic background noises (ECO-SiN sentences). The sentences were embedded at natural speaking levels in six realistic background noises that differed in their overall level, which resulted in a range of fixed signal-to-noise ratios. Ten young, normal hearing participants took part in the study, along with 20 older participants with a range of levels of hearing loss who were tested with and without hearing-aid amplification. We found that scores were driven by hearing loss and the characteristics of the background noise, as expected, but also strongly by the speech materials. Scores obtained with the more realistic sentences were generally lower than those obtained with the standard sentences, which reduced ceiling effects for the majority of environments/listeners (but introduced floor effects in some cases). Because ceiling and floor effects limit the potential for observing changes in performance, benefits of amplification were highly dependent on the speech materials for a given background noise and participant group. Overall, the more realistic speech task offered a better dynamic range for capturing individual performance and hearing-aid benefit across the range of real-world environments we examined.