Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods stru...
The Journal of the Acoustical Society of America
Jan 1, 2024
In indoor environments, reverberation often distorts clean speech. Although deep learning-based speech dereverberation approaches have shown much better performance than traditional ones, the inferior speech quality of the dereverberated speech cause...
Studies in health technology and informatics
Jun 29, 2023
Artificial Intelligence (AI) is a computer system that simulates intelligent human behavior. The use of AI is rapidly shifting Healthcare. Speech recognition (SR) is a type of AI physicians use to operate Electronic Health records (EHR). This paper a...
The Journal of the Acoustical Society of America
May 1, 2023
Recent years have brought considerable advances to our ability to increase intelligibility through deep-learning-based noise reduction, especially for hearing-impaired (HI) listeners. In this study, intelligibility improvements resulting from a curre...
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 wi...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Envelope waveforms can be extracted from multiple frequency bands of a speech signal, and envelope waveforms carry important intelligibility information for human speech communication. This study aimed to investigate whether a deep learning-based mod...
The Journal of the Acoustical Society of America
Mar 1, 2022
Automatic speech recognition (ASR) has made major progress based on deep machine learning, which motivated the use of deep neural networks (DNNs) as perception models and specifically to predict human speech recognition (HSR). This study investigates...
The Journal of the Acoustical Society of America
Nov 1, 2021
The fundamental requirement for real-time operation of a speech-processing algorithm is causality-that it operate without utilizing future time frames. In the present study, the performance of a fully causal deep computational auditory scene analysis...
The Journal of the Acoustical Society of America
Oct 1, 2021
The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize ...
The Journal of the Acoustical Society of America
Jun 1, 2021
Real-time operation is critical for noise reduction in hearing technology. The essential requirement of real-time operation is causality-that an algorithm does not use future time-frame information and, instead, completes its operation by the end of ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.