Spoken word recognition in noise in Mandarin-speaking pediatric cochlear implant users

Ren, Cuncun; Yang, Jing; Zha, Dingjun; Lin, Ying; Liu, Haihong; Kong, Ying; Li, Sha; Xu, Li

doi:10.1016/j.ijporl.2018.07.039

Cited by 19 publications

(10 citation statements)

References 35 publications

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“…However, of these hours, 3.42 are speech in noise. Compared to typical hearing, hearing with a CI reduces the auditory quality of spoken language input and children with CI have more problems than children with TH with perceiving speech in noise (Ren et al, 2018). In addition, in comparison to their peers with TH, children with CI have significantly lower auditory perception scores (Ashori, 2020), which is most likely attributed to the reduced auditory input quality.…”

Section: Discussionmentioning

confidence: 99%

“…A reduced auditory quality might lead to errors in perceptual processing and thereby slow down the development of a structured semantic lexicon. Children with CI also perceive speech in noise less well compared to children with TH (Ren et al, 2018), and might therefore have more problems to make use of the spoken language environment around them. In order to separate the influence of a later language onset from factors such as reduced input quality, it is important to take the hearing age of children with CI into consideration when comparing the structure of their semantic network to peers with TH.…”

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confidence: 99%

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Comparing the semantic networks of children with cochlear implants and children with typical hearing: Effects of length of language access.

Socher¹,

Löfkvist²,

Wass³

2021

Preprint

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Purpose: Kenett et al. (2013) report that the sematic network of children with CI is less structured compared to the sematic network of children with TH. This study aims to evaluate if such differences are only evident if children with CI are compared to children with TH matched on chronological age, or also if they are compared to children with TH matched on hearing age. Method: The performance of a group of children with CI on a verbal fluency task was compared to the performance of a group of chronological-age matched children with TH. Subsequently, computational network analysis was used to compare the semantic network structure of the groups. The same procedure was applied to compare a group of children with CI to a group of hearing-age matched children with TH. Results: Children with CI performed significantly more poorly than children with TH matched on chronological age on a semantic fluency task and exhibited a significantly less structured semantic network. No significant difference in performance on a semantic fluency task was found between children with CI and children with TH matched on hearing-age. However, the structure of the semantic network differed significantly for the hearing age matched groups. Conclusions: Although the groups perform on the same level on a sematic fluency task, the semantic network for spoken language of children with CI is less structured compared to children with TH matched on hearing age. Reasons for this might be differences in the (perceptual) quality and the quantity of spoken language input.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Comparing the semantic networks of children with cochlear implants and children with typical hearing: Effects of length of language access.

Socher¹,

Löfkvist²,

Wass³

2021

Preprint

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show abstract

“…A cochlear implant (CI) is a surgically implanted electronic device that stimulates auditory nerves to provide a sense of sound for people with severe-to-profound sensorineural hearing loss. Despite technological and surgical advances since the 1960s, improving the speech perception and intelligibility of CI users in real-world scenarios remains a challenge [1][2][3][4][5][6][7][8][9][10][11][12]. One promising direction is combined electric and acoustic stimulation (EAS) technology.…”

Section: Introductionmentioning

confidence: 99%

Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks

Wang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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The combined electric and acoustic stimulation (EAS) has demonstrated better speech recognition than conventional cochlear implant (CI) and yielded satisfactory performance under quiet conditions. However, when noise signals are involved, both the electric signal and the acoustic signal may be distorted, thereby resulting in poor recognition performance. To suppress noise effects, speech enhancement (SE) is a necessary unit in EAS devices. Recently, a time-domain speech enhancement algorithm based on the fully convolutional neural networks (FCN) with a short-time objective intelligibility (STOI)-based objective function (termed FCN(S) in short) has received increasing attention due to its simple structure and effectiveness of restoring clean speech signals from noisy counterparts. With evidence showing the benefits of FCN(S) for normal speech, this study sets out to assess its ability to improve the intelligibility of EAS simulated speech. Objective evaluations and listening tests were conducted to examine the performance of FCN(S) in improving the speech intelligibility of normal and vocoded speech in noisy environments. The experimental results show that, compared with the traditional minimum-mean square-error SE method and the deep denoising autoencoder SE method, FCN(S) can obtain better gain in the speech intelligibility for normal as well as vocoded speech. This study, being the first to evaluate deep learning SE approaches for EAS, confirms that FCN(S) is an effective SE approach that may potentially be integrated into an EAS processor to benefit users in noisy environments.

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“…The noise issue is a common complaint of CI users (e.g., Ren et al, 2018). Because of variability associated with implant surgery time, hearing history, rehabilitation and training, surgical conditions, devices and signal processing, and so on, large differences in hearing abilities have always been reported within any group of CI users.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Noise Suppression in Cochlear Implants: A Single-Channel Noise Reduction Algorithm1

Zhou

Wang²,

Zheng

et al. 2020

Front. Neurosci.

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The cochlea "translates" the in-air vibrational acoustic "language" into the spikes of neural "language" that are then transmitted to the brain for auditory understanding and/or perception. During this intracochlear "translation" process, high resolution in time-frequency-intensity domains guarantees the high quality of the input neural information for the brain, which is vital for our outstanding hearing abilities. However, cochlear implants (CIs) have coarse artificial coding and interfaces, and CI users experience more challenges in common acoustic environments than their normalhearing (NH) peers. Noise from sound sources that a listener has no interest in may be neglected by NH listeners, but they may distract a CI user. We discuss the CI noise-suppression techniques and introduce noise management for a new implant system. The monaural signal-to-noise ratio estimation-based noise suppression algorithm "eVoice," which is incorporated in the processors of Nurotron R Enduro TM , was evaluated in two speech perception experiments. The results show that speech intelligibility in stationary speech-shaped noise can be significantly improved with eVoice. Similar results have been observed in other CI devices with single-channel noise reduction techniques. Specifically, the mean speech reception threshold decrease in the present study was 2.2 dB. The Nurotron society already has more than 10,000 users, and eVoice is a start for noise management in the new system. Future steps on nonstationary-noise suppression, spatial-source separation, bilateral hearing, microphone configuration, and environment specification are warranted. The existing evidence, including our research, suggests that noise-suppression techniques should be applied in CI systems. The artificial hearing of CI listeners requires more advanced signal processing techniques to reduce brain effort and increase intelligibility in noisy settings.

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Spoken word recognition in noise in Mandarin-speaking pediatric cochlear implant users

Cited by 19 publications

References 35 publications

Comparing the semantic networks of children with cochlear implants and children with typical hearing: Effects of length of language access.

Comparing the semantic networks of children with cochlear implants and children with typical hearing: Effects of length of language access.

Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks

A New Approach for Noise Suppression in Cochlear Implants: A Single-Channel Noise Reduction Algorithm1

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