AIMC Topic: Audiometry, Speech

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Single-ended prediction of listening effort using deep neural networks.

Hearing research Dec 27, 2017
The effort required to listen to and understand noisy speech is an important factor in the evaluation of noise reduction schemes. This paper introduces a model for Listening Effort prediction from Acoustic Parameters (LEAP). The model is based on met...
Acoustic Stimulation Auditory Pathways Hearing Noise Attention Audiometry, Speech Perceptual Masking Hearing Disorders Persons with Hearing Disabilities Speech Perception Models, Psychological Adult Male Case-Control Studies Deep Learning Middle Aged Female Aged Young Adult Humans
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Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users.

Hearing research Nov 30, 2016
Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in nois...
Perceptual Masking Speech Perception Audiometry, Speech Sound Spectrography Young Adult Humans Comprehension Noise Adult Algorithms Neural Networks, Computer Aged Acoustic Stimulation Speech Intelligibility Cochlear Implantation Middle Aged Aged, 80 and over Signal Processing, Computer-Assisted Prosthesis Design Electric Stimulation Cochlear Implants Acoustics Persons with Hearing Disabilities
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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...
Hearing Speech Intelligibility Hearing Aids Comprehension Acoustic Stimulation Hearing Loss, Sensorineural Correction of Hearing Impairment Audiometry, Speech Speech Perception Auditory Threshold Persons with Hearing Disabilities Male Middle Aged Signal Processing, Computer-Assisted Signal-To-Noise Ratio Deep Learning Female Aged Young Adult Humans Perceptual Masking
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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...
Machine Learning Perceptual Masking Female Male Middle Aged Aged Humans Neural Networks, Computer Hearing Loss Persons with Hearing Disabilities Hearing Aids Acoustic Stimulation Recognition, Psychology Electric Stimulation Noise Audiometry, Speech Speech Perception Speech Intelligibility Signal Processing, Computer-Assisted
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