Predicting speech perception in older listeners with sensorineural hearing loss using automatic speech recognition

The objective of this study was to provide proof of concept that the speech intelligibility in quiet of unaided older hearing-impaired (OHI) listeners can be predicted by automatic speech recognition (ASR). Twenty-four OHI listeners completed three speech-identification tasks using speech materials of varying linguistic complexity and predictability (i.e., logatoms, words, and sentences). An ASR system was first trained on different speech materials and then used to recognize the same speech stimuli presented to the listeners but processed to mimic some of the perceptual consequences of age-related hearing loss experienced by each of the listeners: the elevation of hearing thresholds (by linear filtering), the loss of frequency selectivity (by spectrally smearing), and loudness recruitment (by raising the amplitude envelope to a power). Independently of the size of the lexicon used in the ASR system, strong to very strong correlations were observed between human and machine intelligibility scores. However, large root-mean-square errors (RMSEs) were observed for all conditions. The simulation of frequency selectivity loss had a negative impact on the strength of the correlation and the RMSE. Highest correlations and smallest RMSEs were found for logatoms, suggesting that the prediction system reflects mostly the functioning of the peripheral part of the auditory system. In the case of sentences, the prediction of human intelligibility was significantly improved by taking into account cognitive performance. This study demonstrates for the first time that ASR, even when trained on intact independent speech material, can be used to estimate trends in speech intelligibility of OHI listeners

Senescent decline in verbal-emotion identification by older hearing-impaired listeners – do hearing aids help?

Purpose: To assess the ability of older-adult hearing-impaired (OHI) listeners to identify verbal expressions of emotions, and to evaluate whether hearing-aid (HA) use improves identification performance in those listeners.Methods: Twenty-nine OHI listeners, who were experienced bilateral-HA users, participated in the study. They listened to a 20-sentence-long speech passage rendered with six different emotional expressions (“happiness”, “pleasant surprise”, “sadness”, “anger”, “fear”, and “neutral”). The task was to identify the emotion portrayed in each version of the passage. Listeners completed the task twice in random order, once unaided, and once wearing their own bilateral HAs. Seventeen young-adult normal-hearing (YNH) listeners were also tested unaided as controls.Results: Most YNH listeners (89.2%) correctly identified emotions compared to just over half of the OHI listeners (58.7%). Within the OHI group, verbal emotion identification was significantly correlated with age, but not with audibility-related factors. The number of OHI listeners who were able to correctly identify the different emotions did not significantly change when HAs were worn (54.8%).Conclusion: In line with previous investigations using shorter speech stimuli, there were clear age differences in the recognition of verbal emotions, with OHI listeners showing a significant reduction in unaided verbal-emotion identification performance that progressively declined with age across older adulthood. Rehabilitation through HAs did not provide compensation for the impaired ability to perceive emotions carried by speech sounds.</div

Improving hearing-aid gains based on automatic speech recognition

This study provides proof of concept that automatic speech recognition (ASR) can be used to improve hearing aid (HA) fitting. A signal-processing chain consisting of a HA simulator, a hearing-loss simulator, and an ASR system normalizing the intensity of input signals was used to find HA-gain functions yielding the highest ASR intelligibility scores for individual audiometric profiles of 24 listeners with age-related hearing loss. Significantly higher aided speech intelligibility scores and subjective ratings of speech pleasantness were observed when the participants were fitted with ASR-established gains than when fitted with the gains recommended by the CAM2 fitting rule

Image_1_OPRA-RS: A Hearing-Aid Fitting Method Based on Automatic Speech Recognition and Random Search.pdf

Hearing-aid (HA) prescription rules (such as NAL-NL2, DSL-v5, and CAM2) are used by HA audiologists to define initial HA settings (e.g., insertion gains, IGs) for patients. This initial fitting is later individually adjusted for each patient to improve clinical outcomes in terms of speech intelligibility and listening comfort. During this fine-tuning stage, speech-intelligibility tests are often carried out with the patient to assess the benefits associated with different HA settings. As these tests tend to be time-consuming and performance on them depends on the patient's level of fatigue and familiarity with the test material, only a limited number of HA settings can be explored. Consequently, it is likely that a suboptimal fitting is used for the patient. Recent studies have shown that automatic speech recognition (ASR) can be used to predict the effects of IGs on speech intelligibility for patients with age-related hearing loss (ARHL). The aim of the present study was to extend this approach by optimizing, in addition to IGs, compression thresholds (CTs). However, increasing the number of parameters to be fitted increases exponentially the number of configurations to be assessed. To limit the number of HA settings to be tested, three random-search (RS) genetic algorithms were used. The resulting new HA fitting method, combining ASR and RS, is referred to as “objective prescription rule based on ASR and random search" (OPRA-RS). Optimal HA settings were computed for 12 audiograms, representing average and individual audiometric profiles typical for various levels of ARHL severity, and associated ASR performances were compared to those obtained with the settings recommended by CAM2. Each RS algorithm was run twice to assess its reliability. For all RS algorithms, ASR scores obtained with OPRA-RS were significantly higher than those associated with CAM2. Each RS algorithm converged on similar optimal HA settings across repetitions. However, significant differences were observed between RS algorithms in terms of maximum ASR performance and processing costs. These promising results open the way to the use of ASR and RS algorithms for the fine-tuning of HAs with potential speech-intelligibility benefits for the patient.</p