Modeling User Intents as Context in Smartphone-connected Hearing Aids

Abstract

Despite the technological advancement of modern hearing aids, many users leave their devices unused due to little perceived benefit. This problem arises from the limitations of the current fitting procedure that rarely takes into account 1) the perceptual differences between users not explained by measurable hearing loss characteristics and 2) the variation in context-specific preferences within individuals. However, the recent emergence of smartphoneconnected hearings aids opens the door to a new level of context awareness that can facilitate dynamic adaptation of settings to users' changing needs. In this position paper, we discuss how user auditory intents could be modeled as context collected via mobile devices and suggest what kinds of contextual information are relevant when learning situation-specific intents and the corresponding preferences of hearing impaired users. Finally, we illustrate our ideas with several examples of real-life situations experienced by subjects from our study.

Inferring User Intents from Motion in Hearing Healthcare

“…Such user preferences continuously change dependent on the contextual environment, activity, time or cognitive state of the user [6]. It is essential as Korzepa et al [7] has argued to incorporate user intents for predictive modeling. Here, physical motion and activity is a central component.…”

Critical hearing experiences manifest differently across individuals: insights from hearing aid data captured in real-life moments

“…For hearing care purposes, it would be beneficial to collect HA datawhich could span everything from sound level, environment classification and gain, to noise reduction algorithms and microphone directionalityduring these moments. An example could be the advanced adaptive features of modern HAs which are rarely taken into consideration during fitting, and which, without appropriate control, could interfere with the intended effect (Korzepa, et al 2018). The adaptive features modify the gain depending on the sound environment, so when a user complains that it is, for example, too loud in certain situationsit could be that this user needs an adjustment in how different adaptive features are activated and not an overall gain reduction.…”

Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference

“…In certain situations, the user may need something different from what the rationale prescribes. This could obviously be a matter of personal preference since different persons may have different wishes and needs, for example, regarding the frequency shaping and the compression characteristics, but it could also be caused by a mismatch between the listening intention assumed by the fitting rationale and the actual listening intention of the user (Korzepa et al, 2018). The listening intention varies within and between situations and listeners (Wolters, Smeds, Schmidt, Christensen, & Norup, 2016), and predicting the intention based only on the acoustic signal picked up by a microphone is not always possible.…”

“…In recent years, new ways of tailoring hearing-aid processing to individual needs based on direct input from the user or on registration of user behavior have been suggested (e.g., Aldaz, Puria, & Leifer, 2016;Johansen et al, 2017;Korzepa et al, 2018). The increased processing power of hearing aids and, not least, the option to connect hearing aids to a smartphone to integrate the processing power of the smartphone in the entire hearing solution have allowed for more advanced and computationally demanding technologies like machine learning to be applied.…”