Hearing loss is the third most common health condition in the United States, affecting millions and significantly impacting communication, education, career opportunities, and the economy. Hearing aids are a widely used solution, incorporating features such as the wide dynamic range compression (WDRC) algorithm. WDRC adjusts amplification based on the intensity of incoming sounds, amplifying softer sounds to enhance audibility while preventing louder sounds from becoming uncomfortably loud. However, this algorithm may introduce distortions and reduce the signal-to-noise ratio (SNR), which can compromise speech clarity and sound quality. Understanding how WDRC influences distortion and SNR is crucial, as these factors are key determinants of speech understanding and sound quality. It is also essential to examine how these acoustic changes relate to users' speech understanding and sound preferences. To address this, our study employs Tympan, an advanced experimental platform for real-time audio processing. This device allows participants to experience processed audio in real-time. Unlike traditional studies requiring 'reverse engineering' of proprietary algorithms, the Tympan provides direct gain measurements, enabling precise SNR calculations. Most prior research has relied on computer simulations to analyze nonlinear algorithms such as WDRC or examine acoustics and listener perceptions. However, these studies often lack customization to individual hearing profiles. Our research uniquely employs a wearable device with a known, tailored to individual hearing levels, enabling accurate assessment of output SNR and distortion. Moreover, while most studies focus solely on acoustic outcomes, ours is among the few to investigate speech perception across various listening environments.
