Tag: Widex Allure

There are two distinctly different signal processing design philosophies used in modern-day premium hearing aids. A new study looks at how both natural processing and AI approaches perform under the same noisy conditions. By Petri Korhonen, MSc; Francis Kuk, PhD; Christopher Slugocki, PhD Improving communication in noisy environments is one of the central goals of hearing aid design. Despite this shared goal, manufacturers vary in how they attempt to solve the speech‑in‑noise (SiN) problem. Widex’s sound design philosophy centers on preserving the natural auditory cues in the original sound with minimal distortions. If these cues are distorted, listeners may experience increased listening effort, fatigue, and reduced satisfaction, even if speech remains audible. To highlight the importance of natural sound, in a survey of 3,877 Widex hearing aid users, 94% rated sound quality as very or extremely important (Ziegler et al., 2023). Preserving natural sound is important not only for sound quality but also for speech understanding. The auditory system relies on prediction by anticipating upcoming words, sound levels, and spatial cues (Best et al., 2008; Sohoglu et al., 2012). When hearing aids maintain these cues, they provide stable, predictable information, and reduce the need for the brain to reconstruct missing details. In contrast, listening to distorted acoustic cues may require engagement of additional cognitive resources to interpret speech leaving the listener feeling fatigued after social interactions, even if the speech is audible (Peelle, 2018). Achieving Natural Sound Preserving the natural details of the original sounds does not imply that no hearing aid processing is applied. In fact, the opposite is true. Achieving natural and high-quality sound requires coordinated action of several signal processing features, each designed with the shared goal of preserving the details of the original sound. The following features are key in achieving natural sound in the Widex Allure hearing aids. Achieving The Cleanest Possible Input: True Input Technology The quality of hearing aid sound processing is limited by its weakest link. If the input sound level exceeds the hearing aid’s input limit, the sound is clipped and compressed, which is perceived as crackling, popping, or as muffled speech. Widex’s True Input technology was the first to achieve extended 108 dB input dynamic range with 113 dB SPL upper limit and extremely low 5 dB SPL noise floor. This technology improves speech understanding in loud noisy situations in which users might be inclined not to use their hearing aids (Kuk et al., 2015). Reducing Comb‑Filter Artifacts Through Low Processing Delay Open‑fit and vented fittings introduce a mixture of direct unaided and aided, processed sound at the eardrum. When these two sounds arrive at the eardrum at different times due to hearing aid processing delay, listeners perceive an undesired “hollow” or “metallic” coloration known as comb-filtering. Thus, to avoid this, the hearing aid processing delay should be kept to a minimum. Widex hearing aids achieve ~2–5 ms processing delay, shorter than the typical 5–8 ms found in premium hearing aids (Balling et al., 2020). Widex’s ZeroDelay technology in the PureSound program further reduces processing delay to just 0.5 ms, which effectively eliminates comb-filtering and provides noticeably more natural sound in open or vented fittings. Its effect has been demonstrated to improve listening preference (Balling et al., 2021) and spatial perception (Korhonen et al., 2022). According to research, it preserves natural voicing cues in stop consonants (Korhonen et al., 2024), promotes better fidelity of neural speech signal (Zhou et al., 2024; Slugocki et al., 2020), and reduces coloration-pitch distortions for vowel sounds (Slugocki et al., 2023). Compression That Preserves the Signal Envelope (Variable Speed Compression) Compression in hearing aids adjusts the gain so soft inputs become audible and loud ones remain comfortable. Slow‑acting compression adjusts the gain gradually, helping to maintain the temporal envelope of speech. In contrast, fast‑acting compression attempts to ensure audibility by responding to rapid changes in sound level. The drawback of fast-acting compression is that it may smear temporal cues. A substantial body of evidence supports the use of slow-acting compression (Kuk 1998; Windle et al., 2023), which has been Widex’s strategy since Senso, the first digital in‑the‑ear hearing aid (Ludvigsen & Tøpholm, 1997). Widex’s Variable Speed Compression blends both slow- and fast-acting strategies. At and above conversational levels, the slow compressor dominates to preserve temporal structure. For soft sounds, highly modulated sounds, or when there are large level changes, fast-acting compression ensures audibility for softer elements of speech. This dual approach preserves the temporal envelope of speech while still ensuring audibility in situations where speech level changes rapidly (Kuk et al., 2019). … While Achieving Noise Management Preserving natural sound supports communication only when speech cues remain audible. Widex Allure’s Speech Enhancer Pro builds on established noise‑reduction philosophy with updated processing. First, a fast time‑domain analyzer operates across 52 bands for accurate noise estimation. Second, slowly operating Speech Enhancer noise reduction system frequency shapes the sound to optimize Speech Intelligibility Index (SII) for the individual wearer, resulting in both objective and subjective benefits (Peeters et al., 2009). The slower processing helps maintain stable and calm sound experience. Because compression, noise reduction, and adaptive directionality all operate simultaneously, it can be difficult to control where the final hearing aid output is placed relative to the wearer’s residual dynamic range. Widex Speech Enhancer Pro’s third element, the Hearing Threshold Level Optimizer, is designed to place the final output appropriately in each frequency band, keeping speech above threshold at frequencies where noise level is low and keeping the noise floor below threshold where noise is present. This results in more similar speech loudness across noisy conditions, improving the perceived naturalness of the target speech (Korhonen et al., 2025). The Widex HD Locator automatic adaptive directional system is designed to further reduce background noise by continually monitoring the environment and selecting the polar pattern that provides the best SNR. It shifts through directional patterns for full environmental awareness in quiet and the least amount of noise around the listener in challenging environments (Kuk et al., 2005; Kuk et al. 2017). What About Machine Learning in Hearing Aids? Machine learning-based noise reduction has emerged as a novel strategy for speech-in-noise management in commercial hearing aids. These systems rely on deep neural networks (DNNs), which are trained using large datasets of real‑world recordings to distinguish between speech and noise. The hearing aid uses this learned knowledge to reduce noise in everyday listening. Generally, the size, diversity, and realism of the datasets influence how well the system generalizes to everyday listening. Networks trained on limited or artificial datasets may perform acceptably in controlled conditions, but struggle in real‑world noisy communication situations. The DNN-based noise reduction systems vary across manufacturers in how they are designed, trained, and optimized. Developers need to balance between development cost, processing complexity, memory requirement, and battery consumption, which can lead to differences in their real-world performance across manufacturers. Widex Approach Compared to DNN-Based Hearing Aid Approach The Widex Allure RIC R D hearing aid does not use machine learning for noise processing; but based on the Widex design philosophy, its processing that prioritizes the naturalness of the original sound should have already ensured the best hearing aid experience including speech-in-noise listening. Given the growing interest in DNN‑based noise reduction, an important question for clinicians is whether the machine learning-based systems necessarily translate into improved speech‑in‑noise performance. To address this, we compared speech‑in‑noise performance with Widex Allure RIC R D against four competitors’ devices that marketed DNN as their noise management strategy. METHODS Participants Twenty-nine adults with symmetrical sensorineural hearing loss participated. Based on four-frequency pure-tone averages (4FPTAs), 14 had mild-to-moderate loss (≤ 40 dB HL; mean 31.3 dB HL) and 15 had moderate to moderate-to-severe loss (> 40 dB HL; mean 53.1 dB HL). Participants’ average ages were 74.0 yrs in both HL group. Eight participants in the Mild HL group had been using hearing aids for an average of 8.6 years, and 13 in the Mod HL group for an average of 22.2 years. Participants’ MoCA scores ranged from 20 to 30 (mean 26.8 , 26.6 ). Study Hearing Aids Five premium hearing aids were fit using manufacturers’ proprietary gain targets and default settings. The same ear-tips, selected individually according to the Widex fitting software’s recommendations, were used with all devices. All devices used “fixed” speech-in-noise programs, except Widex Allure, which in the absence of a noise program used Universal or the PureSound program (only for mild sloping hearing loss). Feedback tests were conducted for all fittings. Setup Hearing aid fitting and testing took place inside a sound-treated booth (3 x 3 x 2 m; W x L x H). Listeners were surrounded by eight loudspeakers as shown in Figure 1. The target speech was presented from -30°, 0°, or +30° azimuth in a pseudo-random order simulating a group conversation. Every second sentence came from 0°, while the side‑speaker location (±30°) was randomized with an equal number of presentations. The level of the speech was initially 83 dB SPL and thereafter adjusted based on listener performance. Three speech-like temporally decorrelated distractor signals (ISTS, Holube et al., 2010) were presented from 150°, 180°, and 210°. Additionally, cafeteria noise was presented from 90°, 150°, 180°, 210°, and 270° at -10 dB relative to the ISTS signal. The total combined background noise level was 68 dB SPL. Figure 1: The loudspeaker configuration used during the study. Procedure In the first study phase, we estimated the listeners’ speech-in-noise performance intensity (P-I) function using a Bayesian-guided method known as the ezSRT test (Slugocki et al., 2026). Each trial included 24 “low context” sentences taken from the Repeat-Recall Test (Slugocki et al., 2018) presented against the background noise. The performance was scored based on 3 or 4 target words per sentence. At the end of the test, the ezSRT test provides an estimate of the performance-intensity (PI) function including the SNRs corresponding to 90% speech understanding (i.e., speech reception thresholds of SRT90). Because most daily successful communications demand near‑complete comprehension, SRT90 would likely represent the poorest/lowest SNRs needed for effective real‑world communication. Thus, evaluating devices at this SNR level offers clinicians clearer insight into which hearing aids are most likely to deliver realistic benefit for communication in everyday situations. In the second study phase, we measured listener word recognition performance for 32 sentences with each study hearing aid at a fixed SNR corresponding to individual SRT90 of the best-performing device in the first study phase. The experiment followed a single-blind design. The order of HA conditions was counterbalanced across participants. RESULTS The Widex Allure RIC R D is labeled as 'Allure.' The other study hearing aids were labeled HA#2 through HA#5 to ensure anonymity. Signal-to-Noise Ratios (SNR) For 90% Correct Identification The mean P-I functions in Figure 2 showed that Allure and HA#2 provided the best performance, with their P-I curves positioned furthest left. HA#5 showed the poorest performance, while HA#3 and HA#4 fell in the middle. There was greater separation among the P-I curves for the Mod HL group than for the Mild HL group, suggesting greater performance differences among hearing aids in listeners with greater hearing losses. Figure 2. Performance-intensity (P-I) functions for speech-in-noise performance for Mild HL (top) and Mod HL (bottom) groups. Each P-I function was derived from the average participant’s threshold and slope parameters. Differences across hearing aids at SRT90 were more evident in the Mod HL group compared to the Mild HL group (Figure 3). Notably, in the Mod HL Group, Allure and HA#2 did not differ significantly from one another. Allure outperformed all other hearing aids except HA#2 by an average of 5.2 to 8.5 dB in this study. In the Mild HL group, Allure and HA#2 did not differ from each other. Allure significantly outperformed HA#5 by 3.6 dB. Figure 3. Linear Mixed Effects (LME) model analysis plots exploring the significant effect of hearing aid condition (x-axis) on listeners’ speech reception thresholds (SRTs). Points represent marginal means and error bars represent 95% confidence intervals. *p

Tag: Widex Allure

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Comparing Natural Processing and AI Approaches in Speech-In-Noise Performance

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Tag: Widex Allure

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Comparing Natural Processing and AI Approaches in Speech-In-Noise Performance

Widex Names US Ski Jumper Estella Hassrick as Brand Ambassador

Pickleball Pro Back in the Game with Hearing Aids

Widex Expands Allure Portfolio in the United States

Less Is More – Optimizing the Dynamic Range of Hearing Aid Output

The Effect of Speech-To-Noise Ratios On Hearing Aid Noise Reduction Systems in Realistic Environments

Widex Unveils Widex Allure Hearing Aids and Cloud-Based Fitting Software

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