AIMC Topic: Speech Perception
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Improving the performance of hearing aids in noisy environments based on deep learning technology.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
The performance of a deep-learning-based speech enhancement (SE) technology for hearing aid users, called a deep denoising autoencoder (DDAE), was investigated. The hearing-aid speech perception index (HASPI) and the hearing- aid sound quality index ...
Evaluating automatic speech recognition systems as quantitative models of cross-lingual phonetic category perception.
The Journal of the Acoustical Society of America
May 1, 2018
Theories of cross-linguistic phonetic category perception posit that listeners perceive foreign sounds by mapping them onto their native phonetic categories, but, until now, no way to effectively implement this mapping has been proposed. In this pape...
The combined use of virtual reality and EEG to study language processing in naturalistic environments.
Behavior research methods
Apr 1, 2018
When we comprehend language, we often do this in rich settings where we can use many cues to understand what someone is saying. However, it has traditionally been difficult to design experiments with rich three-dimensional contexts that resemble our ...
Speech emotion recognition based on brain and mind emotional learning model.
Journal of integrative neuroscience
Jan 1, 2018
Speech emotion recognition is a challenging obstacle to enabling communication between humans and machines. The present study introduces a new model of speech emotion recognition based on the relationship between the human brain and mind. According t...
Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.
Trends in hearing
Jan 1, 2018
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using...
Objective Prediction of Hearing Aid Benefit Across Listener Groups Using Machine Learning: Speech Recognition Performance With Binaural Noise-Reduction Algorithms.
Trends in hearing
Jan 1, 2018
The simulation framework for auditory discrimination experiments (FADE) was adopted and validated to predict the individual speech-in-noise recognition performance of listeners with normal and impaired hearing with and without a given hearing-aid alg...
Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.
Ear and hearing
Jan 1, 2018
OBJECTIVE: We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI...
A transfer learning approach to goodness of pronunciation based automatic mispronunciation detection.
The Journal of the Acoustical Society of America
Nov 1, 2017
Goodness of pronunciation (GOP) is the most widely used method for automatic mispronunciation detection. In this paper, a transfer learning approach to GOP based mispronunciation detection when applying maximum F1-score criterion (MFC) training to de...
An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker.
The Journal of the Acoustical Society of America
Jun 1, 2017
Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This...
Audiometry, Speech
Perceptual Masking
Speech Perception
Aged
Auditory Threshold
Young Adult
Hearing Loss, Sensorineural
Male
Acoustic Stimulation
Correction of Hearing Impairment
Deep Learning
Middle Aged
Humans
Hearing
Speech Intelligibility
Female
Signal Processing, Computer-Assisted
Hearing Aids
Signal-To-Noise Ratio
Persons with Hearing Disabilities
Comprehension
Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.
The Journal of the Acoustical Society of America
Mar 1, 2017
Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wie...
Audiometry, Speech
Perceptual Masking
Signal Processing, Computer-Assisted
Acoustic Stimulation
Electric Stimulation
Persons with Hearing Disabilities
Humans
Aged
Speech Perception
Speech Intelligibility
Neural Networks, Computer
Male
Recognition, Psychology
Machine Learning
Hearing Aids
Middle Aged
Female
Hearing Loss
Noise
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