AIMC Topic: Phonetics

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Phonetic variability constrained bottleneck features for joint speaker recognition and physical task stress detection.

The Journal of the Acoustical Society of America Nov 1, 2020
Normalizing intrinsic variabilities (e.g., variability in speech production brought on by aging, physical or cognitive task stress, Lombard effect, etc.) in speech and speaker recognition models is essential for system robustness. This study focuses ...
Discriminant Analysis Speech Recognition Software Phonetics Neural Networks, Computer Speech
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EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition.

Cognitive science Apr 1, 2020
Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-ste...
Speech Perception Phonetics Models, Neurological Semantics Computer Simulation Female Male Speech Humans Neural Networks, Computer
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Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations.

Cognitive science Jan 1, 2020
High phonological neighborhood density has been associated with both advantages and disadvantages in early word learning. High density may support the formation and fine-tuning of new word sound memories-a process termed lexical configuration (e.g., ...
Humans Neural Networks, Computer Child Verbal Learning Computer Simulation Learning Phonetics Linguistics
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Semi-supervised learning of a nonnative phonetic contrast: How much feedback is enough?

Attention, perception & psychophysics May 1, 2019
Semi-supervised learning refers to learning that occurs when feedback about performance is provided on only a subset of training trials. Algorithms for semi-supervised learning are popular in machine learning because of their minimal reliance on labe...
Phonetics Knowledge of Results, Psychological Feedback Male Supervised Machine Learning Task Performance and Analysis Female Young Adult Humans Algorithms
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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...
Neural Networks, Computer Language Speech Perception Phonetics Speech Recognition Software Humans
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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...
Pattern Recognition, Automated Judgment Speech Perception Phonetics Humans Acoustics Markov Chains Software Speech Acoustics Voice Quality Speech Production Measurement Deep Learning Signal Processing, Computer-Assisted
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Improved speech inversion using general regression neural network.

The Journal of the Acoustical Society of America Sep 1, 2015
The problem of nonlinear acoustic to articulatory inversion mapping is investigated in the feature space using two models, the deep belief network (DBN) which is the state-of-the-art, and the general regression neural network (GRNN). The task is to e...
Phonetics Speech Acoustics Nonlinear Dynamics Pattern Recognition, Automated Speech Production Measurement Numerical Analysis, Computer-Assisted Female Reproducibility of Results Male Regression Analysis Databases, Factual Computer Simulation Humans Neural Networks, Computer
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