Comparison of speech intelligibility in quiet and in noise after hearing aid fitting according to a purely prescriptive and a comparative fitting procedure.
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ABSTRACT:
SUBMITTER: Metselaar M
PROVIDER: S-EPMC2491429 | biostudies-other | 2008 Sep
REPOSITORIES: biostudies-other
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Project description:OBJECTIVES:The objective was to determine self-adjusted output response and speech intelligibility index (SII) in individuals with mild to moderate hearing loss and to measure the effects of prior hearing aid experience. DESIGN:Thirteen hearing aid users and 13 nonusers, with similar group-mean pure-tone thresholds, listened to prerecorded and preprocessed sentences spoken by a man. Starting with a generic level and spectrum, participants adjusted (1) overall level, (2) high-frequency boost, and (3) low-frequency cut. Participants took a speech perception test after an initial adjustment before making a final adjustment. The three self-selected parameters, along with individual thresholds and real-ear-to-coupler differences, were used to compute output levels and SIIs for the starting and two self-adjusted conditions. The values were compared with an NAL second nonlinear threshold-based prescription (NAL-NL2) and, for the hearing aid users, performance of their existing hearing aids. RESULTS:All participants were able to complete the self-adjustment process. The generic starting condition provided outputs (between 2 and 8 kHz) and SIIs that were significantly below those prescribed by NAL-NL2. Both groups increased SII to values that were not significantly different from prescription. The hearing aid users, but not the nonusers, increased high-frequency output and SII significantly after taking the speech perception test. Seventeen of the 26 participants (65%) met an SII criterion of 60% under the generic starting condition. The proportion increased to 23 out of 26 (88%) after the final self-adjustment. Of the 13 hearing aid users, 8 (62%) met the 60% criterion with their existing hearing aids. With the final self-adjustment, 12 out of 13 (92%) met this criterion. CONCLUSIONS:The findings support the conclusion that user self-adjustment of basic amplification characteristics can be both feasible and effective with or without prior hearing aid experience.
| S-EPMC6289875 | biostudies-literature
Project description:Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural architecture search, using a novel deep learning-based metric for speech intelligibility. The network achieves state-of-the-art denoising on a range of human-graded assessments, generalizes across different noise categories and-in contrast to classic beamforming approaches-operates on a single microphone. The system runs in real time on a laptop, suggesting that large-scale deployment on hearing aid chips could be achieved within a few years. Deep learning-based denoising therefore holds the potential to improve the quality of life of millions of hearing impaired people soon.
| S-EPMC9932078 | biostudies-literature
Project description:Reverberation can have a strong detrimental effect on speech intelligibility in noise. Two main monaural effects were studied here: the temporal smearing of the target speech, which makes the speech less understandable, and the temporal smearing of the noise, which reduces the opportunity for listening in the masker dips. These phenomena have been shown to affect normal-hearing (NH) listeners. The aim of this study was to understand whether hearing-impaired (HI) listeners are more affected by reverberation, and if so to identify which of these two effects is responsible. They were investigated separately and in combination, by applying reverberation either on the target speech, on the noise masker, or on both sources. Binaural effects were not investigated here. Intelligibility scores in the presence of stationary and modulated noise were systematically compared for both NH and HI listeners in these situations. At the optimal signal-to-noise ratios (SNRs) (that is to say, the SNRs with the least amount of floor and ceiling effects), the temporal smearing of both the speech and the noise had a similar effect for the HI and NH listeners, so that reverberation was not more detrimental for the HI listeners. There was only a very limited dip listening benefit at this SNR for either group. Some differences across group appeared at the SNR maximizing dip listening, but they could not be directly related to an effect of reverberation, and were rather due to floor effects or to the reduced ability of the HI listeners to benefit from dip listening, even in the absence of reverberation.
| S-EPMC9428532 | biostudies-literature
Project description:Purpose:There is a growing interest in speech intelligibility and auditory perception of deaf children. The aim of the present study was to compare speech intelligibility and auditory perception of pre-school children with Hearing Aid (HA), Cochlear Implant (CI), and Typical Hearing (TH). Methods:The research design was descriptive-analytic and comparative. The participants comprised 75 male pre-school children aged 4-6 years in the 2017-2018 from Tehran, Iran. The participants were divided into three groups, and each group consisted of 25 children. The first and second groups were respectively selected from pre-school children with HA and CI using the convenience sampling method, while the third group was selected from pre-school children with TH by random sampling method. All children completed Speech Intelligibility Rating and Categories of Auditory Performance Questionnaires. Results:The findings indicated that the mean scores of speech intelligibility and auditory perception of the group with TH were significantly higher than those of the other groups (P < 0.0001). The mean scores of speech intelligibility in the group with CI did not significantly differ from those of the group with HA (P < 0.38). Also, the mean scores of auditory perception in the group with CI were significantly higher than those of the group with HA (P < 0.002). Conclusion:The results showed that auditory perception in children with CI was significantly higher than children with HA. This finding highlights the importance of cochlear implantation at a younger age and its significant impact on auditory perception in deaf children.
| S-EPMC7231984 | biostudies-literature
Project description: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.
| S-EPMC8899657 | biostudies-literature
Project description:Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target or the masker. Speech corrupted at low signal-to-noise ratio (SNR) levels (-5 and 0 dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility (over 60% points in -5 dB babble) over that attained by human listeners with unprocessed stimuli. The findings from this study suggest that algorithms that can estimate reliably the SNR in each T-F unit can improve speech intelligibility.
| S-EPMC2757424 | biostudies-other
Project description:Sensory and motor skills can be improved with training, but learning is often restricted to practice stimuli. As an exception, training on closed-loop (CL) sensorimotor interfaces, such as action video games and musical instruments, can impart a broad spectrum of perceptual benefits. Here we ask whether computerized CL auditory training can enhance speech understanding in levels of background noise that approximate a crowded restaurant. Elderly hearing-impaired subjects trained for 8 weeks on a CL game that, like a musical instrument, challenged them to monitor subtle deviations between predicted and actual auditory feedback as they moved their fingertip through a virtual soundscape. We performed our study as a randomized, double-blind, placebo-controlled trial by training other subjects in an auditory working-memory (WM) task. Subjects in both groups improved at their respective auditory tasks and reported comparable expectations for improved speech processing, thereby controlling for placebo effects. Whereas speech intelligibility was unchanged after WM training, subjects in the CL training group could correctly identify 25% more words in spoken sentences or digit sequences presented in high levels of background noise. Numerically, CL audiomotor training provided more than three times the benefit of our subjects' hearing aids for speech processing in noisy listening conditions. Gains in speech intelligibility could be predicted from gameplay accuracy and baseline inhibitory control. However, benefits did not persist in the absence of continuing practice. These studies employ stringent clinical standards to demonstrate that perceptual learning on a computerized audio game can transfer to "real-world" communication challenges.
| S-EPMC5997394 | biostudies-literature
Project description:ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals.DesignThe EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure.ResultsThe results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear.ConclusionWe showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions.
| S-EPMC9396236 | biostudies-literature
Project description:Dissatisfaction with the sound of one's own voice is common among hearing-aid users. Little is known regarding how hearing impairment and hearing aids separately affect own-voice perception. This study examined own-voice perception and associated issues before and after a hearing-aid fitting for new hearing-aid users and refitting for experienced users to investigate whether it was possible to differentiate between the effect of (unaided) hearing impairment and hearing aids. Further aims were to investigate whether First-Time and Experienced users as well as users with dome and mold inserts differed in the severity of own-voice problems. The study had a cohort design with three groups: First-Time hearing-aid users going from unaided to aided hearing (n = 70), Experienced hearing-aid users replacing their old hearing aids (n = 70), and an unaided control group (n = 70). The control group was surveyed once and the hearing-aid users twice; once before hearing-aid fitting/refitting and once after. The results demonstrated that own-voice problems are common among both First-Time and Experienced hearing-aid users with either dome- or mold-type fittings, while people with near-normal hearing and not using hearing aids report few problems. Hearing aids increased ratings of own-voice problems among First-Time users, particularly those with mold inserts. The results suggest that altered auditory feedback through unaided hearing impairment or through hearing aids is likely both to change own-voice perception and complicate regulation of vocal intensity, but hearing aids are the primary reason for poor perceived sound quality of one's own voice.
| S-EPMC7887686 | biostudies-literature
Project description:Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices.
| S-EPMC5256482 | biostudies-literature