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Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks
- Source :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 29
- Publication Year :
- 2020
-
Abstract
- 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.
- Subjects :
- Computer science
Speech recognition
Noise reduction
medicine.medical_treatment
Biomedical Engineering
Intelligibility (communication)
Convolutional neural network
030507 speech-language pathology & audiology
03 medical and health sciences
0302 clinical medicine
Cochlear implant
Internal Medicine
medicine
Humans
030223 otorhinolaryngology
Noise measurement
business.industry
General Neuroscience
Deep learning
Rehabilitation
Speech Intelligibility
Electric Stimulation
Speech enhancement
Cochlear Implants
Acoustic Stimulation
QUIET
Speech Perception
Artificial intelligence
Neural Networks, Computer
0305 other medical science
business
Subjects
Details
- ISSN :
- 15580210
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
- Accession number :
- edsair.doi.dedup.....c64b2961ee9d2afb5c47a962c68480da