An mHealth App (Speech Banana) for Auditory Training: App Design and Development Study

Digital Therapeutics in Hearing Healthcare: Evidence-Based Review

Digital therapeutics (DTx) in hearing research have emerged as a new category of therapies providing evidence-based intervention via digital means, such as software, smartphone apps, or websites. However, although relatively new, they are not well-established. In this article, we review DTx technologies in hearing research fields, focusing on three categories: prevention and diagnosis, aid (assistance), and cure (digital medicine). We observe that most DTx systems require interactions with users (or patients) without the direct support of clinical professionals to obtain or collect medical evidence; this makes training (or education) features crucial to the therapy's success. In this view, this article discusses the education or training functions of the current DTx and their contribution and purposes. The impact of emerging artificial intelligence (AI) on DTx in hearing research is being explored, and the future of DTx concerning AI integration is being discussed. We believe that this work will contribute to a better understanding of the current and future DTx technological advancements and, in particular, shed light on the field of hearing research.

Guidance on Providing Patient-Centered Care

This article explores the clinical applications of Audiology Practice Standards Organization (APSO) Standards 2 and 13, both of which keep us fully focused on the patient-as-person. The topics within Standard 2 include audiologists' interpersonal communication skills, specifically clarity (ensuring patient comprehension) and empathy (understanding the patient's experiences). Standard 2 also addresses the topic of health literacy, which has been recently expanded to consider not only the degree to which individuals are able to find, understand, and use information and services to inform health-related decisions but also how organizations equitably support those necessary skills. The characteristics and benefits of services provided in patient- and family-centered care contexts are also described. Standard 13 addresses audiologists' support toward overall successful adjustment as defined by the patient, including both technical and personal adjustment to amplification, nontechnical communication issues, and rehabilitation support using readily available technologies. A new assessment protocol that includes goal setting and intervention is described, as well as a patient-centered intervention strategy involving discussion of personally meaningful photographs. Both standards provide a necessary balance to our technology-reliant profession.

Visualization of Speech Perception Analysis via Phoneme Alignment: A Pilot Study

Objective: Speech tests assess the ability of people with hearing loss to comprehend speech with a hearing aid or cochlear implant. The tests are usually at the word or sentence level. However, few tests analyze errors at the phoneme level. So, there is a need for an automated program to visualize in real time the accuracy of phonemes in these tests. Method: The program reads in stimulus-response pairs and obtains their phonemic representations from an open-source digital pronouncing dictionary. The stimulus phonemes are aligned with the response phonemes via a modification of the Levenshtein Minimum Edit Distance algorithm. Alignment is achieved via dynamic programming with modified costs based on phonological features for insertion, deletions and substitutions. The accuracy for each phoneme is based on the F1-score. Accuracy is visualized with respect to place and manner (consonants) or height (vowels). Confusion matrices for the phonemes are used in an information transfer analysis of ten phonological features. A histogram of the information transfer for the features over a frequency-like range is presented as a phonemegram. Results: The program was applied to two datasets. One consisted of test data at the sentence and word levels. Stimulus-response sentence pairs from six volunteers with different degrees of hearing loss and modes of amplification were analyzed. Four volunteers listened to sentences from a mobile auditory training app while two listened to sentences from a clinical speech test. Stimulus-response word pairs from three lists were also analyzed. The other dataset consisted of published stimulus-response pairs from experiments of 31 participants with cochlear implants listening to 400 Basic English Lexicon sentences via different talkers at four different SNR levels. In all cases, visualization was obtained in real time. Analysis of 12,400 actual and random pairs showed that the program was robust to the nature of the pairs. Conclusion: It is possible to automate the alignment of phonemes extracted from stimulus-response pairs from speech tests in real time. The alignment then makes it possible to visualize the accuracy of responses via phonological features in two ways. Such visualization of phoneme alignment and accuracy could aid clinicians and scientists.