AIMC Topic: Speech Intelligibility

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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 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...
Humans Speech Intelligibility Speech Perception Auditory Perception Deep Learning Time Factors
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A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

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
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, su...
Hearing Algorithms Speech Intelligibility Canonical Correlation Analysis Deep Learning
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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 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...
Deep Learning Speech Intelligibility Hearing Loss, Sensorineural Speech Perception Humans Algorithms
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Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

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 ...
Deep Learning Speech Intelligibility Hearing Loss Humans Language Speech Perception Perceptual Masking
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An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners.

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 ...
Speech Perception Hearing Loss, Sensorineural Hearing Aids Hearing Loss Humans Algorithms Hearing Speech Intelligibility Deep Learning
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On training targets for deep learning approaches to clean speech magnitude spectrum estimation.

The Journal of the Acoustical Society of America May 1, 2021
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...
Speech Intelligibility Noise Speech Perception Deep Learning Signal-To-Noise Ratio Speech
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A talker-independent deep learning algorithm to increase intelligibility for hearing-impaired listeners in reverberant competing talker conditions.

The Journal of the Acoustical Society of America Jun 1, 2020
Deep learning based speech separation or noise reduction needs to generalize to voices not encountered during training and to operate under multiple corruptions. The current study provides such a demonstration for hearing-impaired (HI) listeners. Sen...
Speech Intelligibility Algorithms Humans Deep Learning Speech Perception Hearing Loss, Sensorineural Hearing
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A joint-feature learning-based voice conversion system for dysarthric user 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, 2019
Dysarthria speakers suffer from poor communication, and voice conversion (VC) technology is a potential approach for improving their speech quality. This study presents a joint feature learning approach to improve a sub-band deep neural network-based...
Deep Learning Speech Intelligibility Self-Help Devices Dysarthria Voice Humans Speech Production Measurement
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A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.

The Journal of the Acoustical Society of America Mar 1, 2019
For deep learning based speech segregation to have translational significance as a noise-reduction tool, it must perform in a wide variety of acoustic environments. In the current study, performance was examined when target speech was subjected to in...
Hearing Loss, Sensorineural Hearing Aids Speech Perception Aged Humans Male Speech Recognition Software Middle Aged Signal-To-Noise Ratio Female Deep Learning Speech Intelligibility
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Talker change detection: A comparison of human and machine performance.

The Journal of the Acoustical Society of America Jan 1, 2019
The automatic analysis of conversational audio remains difficult, in part, due to the presence of multiple talkers speaking in turns, often with significant intonation variations and overlapping speech. The majority of prior work on psychoacoustic sp...
Natural Language Processing Adult Speech Perception Speech Intelligibility Humans Speech Recognition Software Psychoacoustics Male Female
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