Speech Perception and Hearing Aids

Souza, Pamela E.

doi:10.1007/978-3-319-33036-5_6

Cited by 18 publications

(12 citation statements)

References 164 publications

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“…More recently, a number of products have also offered frequency lowering (either frequency compression or frequency transposition). Each feature is intended to improve aided speech perception and/or sound quality, and for the most part there is evidence for benefit of those features (Bentler, 2005;Bentler, Wu, Kettel, & Hurtig, 2008;Simpson, 2009;Souza, 2002Souza, , 2016. However, each type of processing may be advantageous for some but not all listeners.…”

Section: Individual Response To Signal Processingmentioning

confidence: 99%

Understanding Variability in Individual Response to Hearing Aid Signal Processing in Wearable Hearing Aids

et al. 2019

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Objectives. Previous work has suggested that individual characteristics, including amount of hearing loss, age, and working memory ability, may affect response to hearing aid signal processing. The present study aims to extend work using metrics to quantify cumulative signal modifications under simulated conditions to real hearing aids worn in everyday listening environments. Specifically, the goal was to determine whether individual factors such as working memory, age, and degree of hearing loss play a role in explaining how listeners respond to signal modifications caused by signal processing in real hearing aids, worn in the listener's everyday environment, over a period of time.Design. Participants were older adults (age range 54-90 years) with symmetrical mild-tomoderate sensorineural hearing loss. We contrasted two distinct hearing aid fittings: one designated as mild signal processing and one as strong signal processing. Forty-nine older adults were enrolled in the study and thirty-five participants had valid outcome data for both hearing aid fittings. The difference between the two settings related to the wide dynamic range compression (WDRC) and frequency compression features. Order of fittings was randomly assigned for each participant. Each fitting was worn in the listener's everyday environments for approximately five weeks prior to outcome measurements. The trial was double blind, with neither the participant nor the tester aware of the specific fitting at the time of the outcome testing. Baseline measures included a full audiometric evaluation as well as working memory and spectral and temporal resolution. The outcome was aided speech recognition in noise. Individual response to signal processing 4Results. The two hearing aid fittings resulted in different amounts of signal modification, with significantly less modification for the mild signal processing fitting. The effect of signal processing on speech intelligibility depended on an individual's age, working memory capacity, and degree of hearing loss. Adults who were older demonstrated progressively poorer speech recognition at high levels of signal modification. Working memory interacted with signal processing, with individuals with lower working memory demonstrating low speech intelligibility in noise with both processing conditions, and individuals with higher working memory demonstrating better speech intelligibility in noise with the mild signal processing fitting. Amount of hearing loss interacted with signal processing, but the effects were very small. Individual spectral and temporal resolution did not contribute significantly to the variance in the speech intelligibility score.Conclusions. When the consequences of a specific set of hearing aid signal processing characteristics were quantified in terms of overall signal modification, there was a relationship between participant characteristics and recognition of speech at different levels of signal modification. Because the hearing aid fittings used were constrained to specific fitting pa...

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Section: Individual Response To Signal Processingmentioning

confidence: 99%

Understanding Variability in Individual Response to Hearing Aid Signal Processing in Wearable Hearing Aids

et al. 2019

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“…Then, the first hidden layer is considered as the input layer for the second DAE, which constructs the second DAE with the second hidden layer and so on, until the i-th DAE is trained. After the neural network training is completed, the weight matrix and deviation vector of a single neural network are taken to be the initial value of DDAE, and the calculated value of the DDAE sequence (fine-tuning) [25], respectively. More details can be found in [24].…”

Section: Effect Of Depth (Layer By Layer Pre-training)mentioning

confidence: 99%

“…where i denotes the frame in the FFT transform, more details can be found in [25]. The frequency range is 0.1 to 1 Hz.…”

Section: Wiener Filter (Wf)mentioning

confidence: 99%

Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders

Lazim

Zhu

2020

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In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid approach for hearing aid applications based on two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three DDAEs with different window lengths, each of which is specialized for a specific enhancement task. Two typical high-frequency hearing loss audiograms were used to test the performance of the approach: Audiogram 1 = (0, 0, 0, 60, 80, 90) and Audiogram 2 = (0, 15, 30, 60, 80, 85). The hearing-aid speech perception index, the hearing-aid speech quality index, and the perceptual evaluation of speech quality were used to evaluate the performance. The experimental results show that the proposed method achieved significantly better results compared with the Wiener filter or a single deep denoising autoencoder alone.

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“…There is tremendous variability in degraded speech perception among older adults. This variability is associated with sensory and cognitive factors ( Committee on Hearing, Bioacoustics and Biomechanics (CHABA), 1988 ; Souza, 2016 ), as well as with individual differences in perceptual learning for speech ( Karawani et al, 2017 ; Manheim et al, 2018 ; Rotman et al, 2020b ). Hearing aids are the most common rehabilitation for speech perception difficulties in older adults with age-related hearing loss ( Souza, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Speech Perception in Older Adults: An Interplay of Hearing, Cognition, and Learning?

2022

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Older adults with age-related hearing loss exhibit substantial individual differences in speech perception in adverse listening conditions. We propose that the ability to rapidly adapt to changes in the auditory environment (i.e., perceptual learning) is among the processes contributing to these individual differences, in addition to the cognitive and sensory processes that were explored in the past. Seventy older adults with age-related hearing loss participated in this study. We assessed the relative contribution of hearing acuity, cognitive factors (working memory, vocabulary, and selective attention), rapid perceptual learning of time-compressed speech, and hearing aid use to the perception of speech presented at a natural fast rate (fast speech), speech embedded in babble noise (speech in noise), and competing speech (dichotic listening). Speech perception was modeled as a function of the other variables. For fast speech, age [odds ratio (OR) = 0.79], hearing acuity (OR = 0.62), pre-learning (baseline) perception of time-compressed speech (OR = 1.47), and rapid perceptual learning (OR = 1.36) were all significant predictors. For speech in noise, only hearing and pre-learning perception of time-compressed speech were significant predictors (OR = 0.51 and OR = 1.53, respectively). Consistent with previous findings, the severity of hearing loss and auditory processing (as captured by pre-learning perception of time-compressed speech) was strong contributors to individual differences in fast speech and speech in noise perception. Furthermore, older adults with good rapid perceptual learning can use this capacity to partially offset the effects of age and hearing loss on the perception of speech presented at fast conversational rates. Our results highlight the potential contribution of dynamic processes to speech perception.

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Speech Perception and Hearing Aids

Cited by 18 publications

References 164 publications

Understanding Variability in Individual Response to Hearing Aid Signal Processing in Wearable Hearing Aids

Understanding Variability in Individual Response to Hearing Aid Signal Processing in Wearable Hearing Aids

Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders

Speech Perception in Older Adults: An Interplay of Hearing, Cognition, and Learning?

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