Enhancing Speech Recorded from a Wearable Sensor Using a Collection of Autoencoders
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1087 CCIS, Page: 383-397
2020
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures4
- Readers4
Conference Paper Description
Assistive Technology (AT) is a concept which includes the use of technological devices to improve the learning process or the general capabilities of people with disabilities. One of the major tasks of the AT is the development of devices that offer alternative or augmentative communication capabilities. In this work, we implemented a simple AT device with a low-cost sensor for registering speech signals, in which the sound is perceived as low quality and corrupted. Thus, it is not suitable to integrate into speech recognition systems, automatic transcription or general recognition of vocal-tract sounds for people with disabilities. We propose the use of a group of artificial neural networks that improve different aspects of the signal. In the study of the speech enhancement, it is normal to focus on how to make improvements in specific conditions of the signal, such as background noise, reverberation, natural noises, among others. In this case, the conditions that degrade the sound are unknown. This uncertainty represents a bigger challenge for the enhancement of the speech, in a real-life application. The results show the capacity of the artificial neural networks to enhance the quality of the sound, under several objective evaluation measurements. Therefore, this proposal can become a way of treating these kinds of signals to improve robust speech recognition systems and increase the real possibilities for implementing low-cost AT devices.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85081179523&origin=inward; http://dx.doi.org/10.1007/978-3-030-41005-6_26; http://link.springer.com/10.1007/978-3-030-41005-6_26; https://dx.doi.org/10.1007/978-3-030-41005-6_26; https://link.springer.com/chapter/10.1007/978-3-030-41005-6_26
Springer Science and Business Media LLC
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