AIMC Topic: Speech Perception

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The Progress of Speech Recognition in Health Care: Surgery as an Example.

Studies in health technology and informatics
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...

Progress made in the efficacy and viability of deep-learning-based noise reduction.

The Journal of the Acoustical Society of America
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...

Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods.

Trends in hearing
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...

A Deep Learning Based Approach to Synthesize Intelligible Speech with Limited Temporal Envelope Information.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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...

A model of speech recognition for hearing-impaired listeners based on deep learning.

The Journal of the Acoustical Society of America
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...

A causal and talker-independent speaker separation/dereverberation deep learning algorithm: Cost associated with conversion to real-time capable operation.

The Journal of the Acoustical Society of America
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...

Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The Journal of the Acoustical Society of America
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 ...

An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners.

The Journal of the Acoustical Society of America
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 ...

On training targets for deep learning approaches to clean speech magnitude spectrum estimation.

The Journal of the Acoustical Society of America
Estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Tra...

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

Proceedings of the National Academy of Sciences of the United States of America
Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get bet...