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Speech

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Parkinson's Disease Classification using Pitch Synchronous Speech Segments and Fine Gaussian Kernels based SVM.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Researchers have been using signal processing based methods to assess speech from Parkinson's disease (PD) patients and identify the contrasting features in comparison to speech from healthy controls (HC). The methodologies follow conventional approa...

Spoken words as biomarkers: using machine learning to gain insight into communication as a predictor of anxiety.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The goal of this study was to explore whether features of recorded and transcribed audio communication data extracted by machine learning algorithms can be used to train a classifier for anxiety.

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition.

Cognitive science
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...

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review.

Journal of Alzheimer's disease : JAD
BACKGROUND: Language is a valuable source of clinical information in Alzheimer's disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis.

Decoding Speech from Single Trial MEG Signals Using Convolutional Neural Networks and Transfer Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Decoding speech directly from the brain has the potential for the development of the next generation, more efficient brain computer interfaces (BCIs) to assist in the communication of patients with locked-in syndrome (fully paralyzed but aware). In t...

A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic cough detection is key to tracking the condition of patients suffering from tuberculosis. We evaluate various acoustic features for performing cough detection using deep architectures. As most previous studies have adopted features designed...

Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning.

The Journal of the Acoustical Society of America
The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach...

Live human-robot interactive public demonstrations with automatic emotion and personality prediction.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Communication with humans is a multi-faceted phenomenon where the emotions, personality and non-verbal behaviours, as well as the verbal behaviours, play a significant role, and human-robot interaction (HRI) technologies should respect this complexit...

Brain activity during reciprocal social interaction investigated using conversational robots as control condition.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (human-human interaction, HHI) to an interaction with a conversational robot (human-robot interaction, ...