AIMC Topic: Speech

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μ-law SGAN for generating spectra with more details in speech enhancement.

Neural networks : the official journal of the International Neural Network Society
The goal of monaural speech enhancement is to separate clean speech from noisy speech. Recently, many studies have employed generative adversarial networks (GAN) to deal with monaural speech enhancement tasks. When using generative adversarial networ...

Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach.

Journal of medical Internet research
BACKGROUND: The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech de...

Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation.

Sensors (Basel, Switzerland)
Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) chil...

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients.

IEEE journal of biomedical and health informatics
Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone influence the meaning of the spoken utterance and overall speech clarity. ...

Deep-learning-based segmentation of the vocal tract and articulators in real-time magnetic resonance images of speech.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Magnetic resonance (MR) imaging is increasingly used in studies of speech as it enables non-invasive visualisation of the vocal tract and articulators, thus providing information about their shape, size, motion and position....

Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) plays a significant role in human-machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotio...

An Investigation of Speech Features, Plant System Alarms, and Operator-System Interaction for the Classification of Operator Cognitive Workload During Dynamic Work.

Human factors
OBJECTIVE: To investigate speech features, human-machine alarms, and operator-system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios.

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identif...

Inner speech.

Wiley interdisciplinary reviews. Cognitive science
Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "little voice in the head," and so on. It is both a familiar element of first-person experience and a psy...

Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition i...