Simulations with FADE of the effect of impaired hearing on speech recognition performance cast doubt on the role of spectral resolution

Hülsmeier, David; Warzybok, Anna; Kollmeier, Birger; Schädler, Marc René

doi:10.1016/j.heares.2020.107995

Cited by 8 publications

(7 citation statements)

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“…Other mechanisms that reduce the information encoded in the internal signal representation, like a reduced spectral resolution, might also be considered in this modeling approach. However, Hülsmeier et al (2020) found that, with FADE, a reduced spectral resolution had only little effect on the simulated SRTs compared to an increased level uncertainty. This indicates that between a reduced spectral resolution and the level uncertainty, the latter is considered to be the suitable mechanism to implement a class D loss in FADE.…”

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confidence: 88%

Thoughts on the potential to compensate a hearing loss in noise

Schädler

2021

F1000Res

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Background: The effect of hearing impairment on speech perception was described by Plomp (1978) as a sum of a loss of class A, due to signal attenuation, and a loss of class D, due to signal distortion. While a loss of class A can be compensated by linear amplification, a loss of class D, which severely limits the benefit of hearing aids in noisy listening conditions, cannot. The hearing loss of class D is assumed to be the main reason why not few users of hearing aids keep complaining about the limited benefit of their devices in noisy environments. Working compensation strategies against it are unknown. Methods: Recently, in an approach to model human speech recognition by means of a re-purposed automatic speech recognition (ASR) system, the loss of class D was explained by introducing a level uncertainty which reduces the individual accuracy of spectro-temporal signal levels. Based on this finding, an implementation of a patented dynamic range manipulation scheme (PLATT) is proposed which aims to mitigate the effect of increased level uncertainty on speech recognition in noise by expanding spectral modulation patterns in the range of 2 to 4 ERB. This compensation approach is objectively evaluated regarding the benefit in speech recognition thresholds in noise using the ASR-based speech recognition model. Recommendations for an evaluation with human listeners are derived. Results: The objective evaluation suggests that approximately half of the class D loss due to an increased level uncertainty might be compensable. To measure the effect with human listeners, an experiment needs to be carefully designed to prevent the confusion class A and D loss compensations. Conclusions: A working compensation strategy for the class D loss could provide previously unexploited potential for relief. Evidence has to be provided in experiments with human listeners.

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confidence: 88%

Thoughts on the potential to compensate a hearing loss in noise

Schädler

2021

F1000Res

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“…The features on the acoustic side were extracted using a Log-Mel spectrogram, as done in Schädler et al. (2016b) and Hülsmeier et al. (2020) and concatenated to the electric features.…”

Section: Methodsmentioning

confidence: 99%

Modelling speech reception thresholds and their improvements due to spatial noise reduction algorithms in bimodal cochlear implant users

et al. 2022

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“…To sum up, it can be seen that in the process of recognizing spoken English, current speech recognition systems mostly use speech recognition technology to innovate in speech feature extraction, while ignoring its internal speech relevance and innovation [ 13 , 14 ]. In addition, in terms of English feature recognition, although it can realize the local feature recognition of most spoken English, there is still no universal speech recognition intelligent model with high recognition accuracy [ 15 – 17 ].…”

Section: Related Workmentioning

confidence: 99%

Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things

Xue

2022

Computational Intelligence and Neuroscience

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With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition.

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Simulations with FADE of the effect of impaired hearing on speech recognition performance cast doubt on the role of spectral resolution

Cited by 8 publications

References 83 publications

Thoughts on the potential to compensate a hearing loss in noise

Thoughts on the potential to compensate a hearing loss in noise

Modelling speech reception thresholds and their improvements due to spatial noise reduction algorithms in bimodal cochlear implant users

Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things

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