An overview of the HASPI and HASQI metrics for predicting speech intelligibility and speech quality for normal hearing, hearing loss, and hearing aids

Kates, James M.; Arehart, Kathryn H.

doi:10.1016/j.heares.2022.108608

Cited by 10 publications

(8 citation statements)

References 75 publications

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“…In contrast, for both the simulated NH listeners and simulated listeners with mild hearing loss, the combination of PEM–AFC and DeepMFC(1) led to a much higher maximum stable gain than for either method alone. The finding that HASQI scores were higher for simulated hearing-impaired listeners than for simulated NH listeners is consistent with the results presented by Kates & Arehart (2022 ). As explained by Kates & Arehart (2022 ), these higher scores may have occurred because the simulated hearing-impaired listeners were less sensitive than the simulated NH listeners to signal degradations such as increased gain at certain frequencies (coloration), owing to the simulated reduced frequency selectivity of the former.…”

Section: Resultssupporting

confidence: 91%

“…The finding that HASQI scores were higher for simulated hearing-impaired listeners than for simulated NH listeners is consistent with the results presented by Kates & Arehart (2022 ). As explained by Kates & Arehart (2022 ), these higher scores may have occurred because the simulated hearing-impaired listeners were less sensitive than the simulated NH listeners to signal degradations such as increased gain at certain frequencies (coloration), owing to the simulated reduced frequency selectivity of the former. Also, for the simulated hearing-impaired listeners, some distortion spectral components would have had levels below the hearing threshold, and thus would not be perceived ( Tan & Moore, 2008 ).…”

Section: Resultssupporting

confidence: 91%

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Evaluation of deep marginal feedback cancellation for hearing aids using speech and music

Zheng

Wang

et al. 2023

Trends in Hearing

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Speech and music both play fundamental roles in daily life. Speech is important for communication while music is important for relaxation and social interaction. Both speech and music have a large dynamic range. This does not pose problems for listeners with normal hearing. However, for hearing-impaired listeners, elevated hearing thresholds may result in low-level portions of sound being inaudible. Hearing aids with frequency-dependent amplification and amplitude compression can partly compensate for this problem. However, the gain required for low-level portions of sound to compensate for the hearing loss can be larger than the maximum stable gain of a hearing aid, leading to acoustic feedback. Feedback control is used to avoid such instability, but this can lead to artifacts, especially when the gain is only just below the maximum stable gain. We previously proposed a deep-learning method called DeepMFC for controlling feedback and reducing artifacts and showed that when the sound source was speech DeepMFC performed much better than traditional approaches. However, its performance using music as the sound source was not assessed and the way in which it led to improved performance for speech was not determined. The present paper reveals how DeepMFC addresses feedback problems and evaluates DeepMFC using speech and music as sound sources with both objective and subjective measures. DeepMFC achieved good performance for both speech and music when it was trained with matched training materials. When combined with an adaptive feedback canceller it provided over 13 dB of additional stable gain for hearing-impaired listeners.

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 91%

Evaluation of deep marginal feedback cancellation for hearing aids using speech and music

Zheng

Wang

et al. 2023

Trends in Hearing

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“…There have been numerous initiatives that have attempted to quantify overall distortion and relate it to speech intelligibility and perceived quality; see Kates and Arehart ( 148 ) for a summary. Perhaps most promising are the Hearing Aid Speech Perception Index (HASPI) and the Hearing Aid Speech Quality Index (HASQI) ( 148 ).…”

Section: Implications For Hearing Aid Fittingmentioning

confidence: 99%

A review of auditory processing and cognitive change during normal ageing, and the implications for setting hearing aids for older adults

2023

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Throughout our adult lives there is a decline in peripheral hearing, auditory processing and elements of cognition that support listening ability. Audiometry provides no information about the status of auditory processing and cognition, and older adults often struggle with complex listening situations, such as speech in noise perception, even if their peripheral hearing appears normal. Hearing aids can address some aspects of peripheral hearing impairment and improve signal-to-noise ratios. However, they cannot directly enhance central processes and may introduce distortion to sound that might act to undermine listening ability. This review paper highlights the need to consider the distortion introduced by hearing aids, specifically when considering normally-ageing older adults. We focus on patients with age-related hearing loss because they represent the vast majority of the population attending audiology clinics. We believe that it is important to recognize that the combination of peripheral and central, auditory and cognitive decline make older adults some of the most complex patients seen in audiology services, so they should not be treated as “standard” despite the high prevalence of age-related hearing loss. We argue that a primary concern should be to avoid hearing aid settings that introduce distortion to speech envelope cues, which is not a new concept. The primary cause of distortion is the speed and range of change to hearing aid amplification (i.e., compression). We argue that slow-acting compression should be considered as a default for some users and that other advanced features should be reconsidered as they may also introduce distortion that some users may not be able to tolerate. We discuss how this can be incorporated into a pragmatic approach to hearing aid fitting that does not require increased loading on audiology services.

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“…For the deep-learning methods, the benefit of speech enhancement reduced dramatically for high SNRs and only a small improvement was obtained when the SNR was 10 dB. Finally, the HASQI scores increased with increasing hearing loss, perhaps because the speech degradation produced by signal processing has a smaller perceptual effect for listeners with more severe hearing loss (Kates & Arehart, 2022).…”

Section: Results and Analysismentioning

confidence: 99%

Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods

Zheng,

Zhang,

Liu

et al. 2023

Trends in Hearing

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Frequency-domain monaural speech enhancement has been extensively studied for over 60 years, and a great number of methods have been proposed and applied to many devices. In the last decade, monaural speech enhancement has made tremendous progress with the advent and development of deep learning, and performance using such methods has been greatly improved relative to traditional methods. This survey paper first provides a comprehensive overview of traditional and deep-learning methods for monaural speech enhancement in the frequency domain. The fundamental assumptions of each approach are then summarized and analyzed to clarify their limitations and advantages. A comprehensive evaluation of some typical methods was conducted using the WSJ + Deep Noise Suppression (DNS) challenge and Voice Bank + DEMAND datasets to give an intuitive and unified comparison. The benefits of monaural speech enhancement methods using objective metrics relevant for normal-hearing and hearing-impaired listeners were evaluated. The objective test results showed that compression of the input features was important for simulated normal-hearing listeners but not for simulated hearing-impaired listeners. Potential future research and development topics in monaural speech enhancement are suggested.

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An overview of the HASPI and HASQI metrics for predicting speech intelligibility and speech quality for normal hearing, hearing loss, and hearing aids

Cited by 10 publications

References 75 publications

Evaluation of deep marginal feedback cancellation for hearing aids using speech and music

Evaluation of deep marginal feedback cancellation for hearing aids using speech and music

A review of auditory processing and cognitive change during normal ageing, and the implications for setting hearing aids for older adults

Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods

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