Home-Based Speech Perception Monitoring for Clinical Use With Cochlear Implant Users

Abstract

Speech-perception testing is essential for monitoring outcomes with a hearing aid or cochlear implant (CI). However, clinical care is time-consuming and often challenging with an increasing number of clients. A potential approach to alleviating some clinical care and possibly making room for other outcome measures is to employ technologies that assess performance in the home environment. In this study, we investigate 3 different speech perception indices in the same 40 CI users: phoneme identification (vowels and consonants), digits in noise (DiN) and sentence recognition in noise (SiN). The first two tasks were implemented on a tablet and performed multiple times by each client in their home environment, while the sentence task was administered at the clinic. Speech perception outcomes in the same forty CI users showed that DiN assessed at home can serve as an alternative to SiN assessed at the clinic. DiN scores are in line with the SiN ones by 3–4 dB improvement and are useful to monitor performance at regular intervals and to detect changes in auditory performance. Phoneme identification in quiet also explains a significant part of speech perception in noise, and provides additional information on the detectability and discriminability of speech cues. The added benefit of the phoneme identification task, which also proved to be easy to administer at home, is the information transmission analysis in addition to the summary score. Performance changes for the different indices can be interpreted by comparing against measurement error and help to target personalized rehabilitation. Altogether, home-based speech testing is reliable and proves powerful to complement care in the clinic for CI users.

Measures of Speech Understanding in Noise for Young Children with a Cochlear Implant in Mainstream and Special Education

“…Results indicate that the DTT was feasible for all children tested in this study, as seen by the familiarity of all the digits, the high stability of the test results (<2 dB SNR), and a small measurement error (≤2 dB SNR). Moreover, the SRTs obtained for children with CIs in this study (average SRT: −2.8 ± 4.1 dB SNR) are very similar to the values obtained in previous research with adults with CIs (average SRT: −3.2 ± 4.4 dB SNR) ( van Wieringen et al, 2021 ). In addition, remembering full triplets did not form a problem for any of the three groups, which was visible in a high percentage of trials in which all digits were identified correctly.…”

“…Previous research with adults showed a high correlation between the DTT results and a sentence-in-noise recognition task ( Smits et al, 2004 ; van Wieringen et al, 2021 ). Sentences are, of all types of speech material, the most closely related to capturing some real-world listening difficulties.…”

“…The digit triplet test (DTT) is one of the paradigms used to assess the SPIN understanding abilities with little influence from linguistic skills ( Cullington & Agyemang-Prempeh, 2017 ; De Graaff et al, 2018 ; Kaandorp et al, 2015 ; van Wieringen et al, 2021 ). The DTT is designed as a self-test, meaning it is made to determine the speech reception threshold (SRT) without a test leader.…”

Comparing Phoneme and Word Recognition Test Outcomes in Adult CI users: Data Analysis from the AuDieT Study

“…It would be little effort to implement the PRQ test on a mobile application and let CI users perform it at home; this would let clinicians have an overview of their patients’ speech recognition issues without devoting time to administering the test in a clinical environment. Research has shown that at-home tests have the potential to be as reliable as those run in a clinic (van Wieringen et al, 2021; Wasmann et al, 2024).…”

“…In order to extract human-readable information from these data points, several data transformation techniques were applied. These include the calculation of accuracy (defined as the percentage of correctly identified phonemes), fitness (also describable as weighted accuracy, described in detail below), and error dispersion (defined in information theory as "the effective number of error classes per stimulus token" (Van Son, 1995)).…”