AIMC Topic: Speech

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A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.

Journal of speech, language, and hearing research : JSLHR
PURPOSE: Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details....

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

IEEE transactions on bio-medical engineering
OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) wit...

Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language.

Sensors (Basel, Switzerland)
Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Biometric analysis, education, security, healthcare, and smart cities are only a few examples of speech recognition applications. Most s...

End-to-End Sentence-Level Multi-View Lipreading Architecture with Spatial Attention Module Integrated Multiple CNNs and Cascaded Local Self-Attention-CTC.

Sensors (Basel, Switzerland)
Concomitant with the recent advances in deep learning, automatic speech recognition and visual speech recognition (VSR) have received considerable attention. However, although VSR systems must identify speech from both frontal and profile faces in re...

Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Assessing the severity level of dysarthria can provide an insight into the patient's improvement, assist pathologists to plan therapy, and aid automatic dysarthric speech recognition systems. In this article, we present a comparative study on the cla...

Robot touch with speech boosts positive emotions.

Scientific reports
A gentle touch is an essential part of human interaction that produces a positive care effect. Previously, robotics studies have shown that robots can reproduce a gentle touch that elicits similar, positive emotional responses in humans. However, whe...

Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for trackin...

End-to-End Lip-Reading Open Cloud-Based Speech Architecture.

Sensors (Basel, Switzerland)
Deep learning technology has encouraged research on noise-robust automatic speech recognition (ASR). The combination of cloud computing technologies and artificial intelligence has significantly improved the performance of open cloud-based speech rec...

Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model.

Computational intelligence and neuroscience
Twitter's popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an in...

Evaluation of text-to-gesture generation model using convolutional neural network.

Neural networks : the official journal of the International Neural Network Society
Conversational gestures have a crucial role in realizing natural interactions with virtual agents and robots. Data-driven approaches, such as deep learning and machine learning, are promising in constructing the gesture generation model, which automa...