AIMC Topic: Speech

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Speech emotion analysis using convolutional neural network (CNN) and gamma classifier-based error correcting output codes (ECOC).

Scientific reports
Speech emotion analysis is one of the most basic requirements for the evolution of Artificial Intelligence (AI) in the field of human-machine interaction. Accurate emotion recognition in speech can be effective in applications such as online support,...

Reliability and validity of a widely-available AI tool for assessment of stress based on speech.

Scientific reports
Cigna's online stress management toolkit includes an AI-based tool that purports to evaluate a person's psychological stress level based on analysis of their speech, the Cigna StressWaves Test (CSWT). In this study, we evaluate the claim that the CSW...

Towards audio-based identification of Ethio-Semitic languages using recurrent neural network.

Scientific reports
In recent times, there is an increasing interest in employing technology to process natural language with the aim of providing information that can benefit society. Language identification refers to the process of detecting which speech a speaker app...

A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations.

Scientific reports
Humans express their emotions in various ways, such as through facial expressions and voices. In particular, emotions are directly expressed or indirectly implied in the text of utterance. Research on the technology to identify emotions included in h...

Speech extraction from vibration signals based on deep learning.

PloS one
Extracting speech information from vibration response signals is a typical system identification problem, and the traditional method is too sensitive to deviations such as model parameters, noise, boundary conditions, and position. A method was propo...

Speech and language processing with deep learning for dementia diagnosis: A systematic review.

Psychiatry research
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its p...

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

Sensors (Basel, Switzerland)
In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extrac...

TranStutter: A Convolution-Free Transformer-Based Deep Learning Method to Classify Stuttered Speech Using 2D Mel-Spectrogram Visualization and Attention-Based Feature Representation.

Sensors (Basel, Switzerland)
Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The res...

Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

Journal of neural engineering
Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from...