AIMC Topic: Speech

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A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing.

Computational intelligence and neuroscience
Dialog state tracking in a spoken dialog system is the task that tracks the flow of a dialog and identifies accurately what a user wants from the utterance. Since the success of a dialog is influenced by the ability of the system to catch the require...

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface.

Sensors (Basel, Switzerland)
People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation ...

Multimodal Assessment of Parkinson's Disease: A Deep Learning Approach.

IEEE journal of biomedical and health informatics
Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can...

Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning.

Methods (San Diego, Calif.)
Due to the complex and intricate nature associated with their production, the acoustic-prosodic properties of a speech signal are modulated with a range of health related effects. There is an active and growing area of machine learning research in th...

Design of deep echo state networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, we provide a novel approach to the architectural design of deep Recurrent Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir Computing framework and inspired by the principles related to the inher...

Confidence in uncertainty: Error cost and commitment in early speech hypotheses.

PloS one
Interactions with artificial agents often lack immediacy because agents respond slower than their users expect. Automatic speech recognisers introduce this delay by analysing a user's utterance only after it has been completed. Early, uncertain hypot...

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors.

Sensors (Basel, Switzerland)
Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors c...

Threshold-Based Noise Detection and Reduction for Automatic Speech Recognition System in Human-Robot Interactions.

Sensors (Basel, Switzerland)
This work develops a speech recognition system that uses two procedures of proposed noise detection and combined noise reduction. The system can be used in applications that require interactive robots to recognize the contents of speech that includes...

Automated depression analysis using convolutional neural networks from speech.

Journal of biomedical informatics
To help clinicians to efficiently diagnose the severity of a person's depression, the affective computing community and the artificial intelligence field have shown a growing interest in designing automated systems. The speech features have useful in...

Encoding of Articulatory Kinematic Trajectories in Human Speech Sensorimotor Cortex.

Neuron
When speaking, we dynamically coordinate movements of our jaw, tongue, lips, and larynx. To investigate the neural mechanisms underlying articulation, we used direct cortical recordings from human sensorimotor cortex while participants spoke natural ...