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Speech

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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...

Artificial intelligence based multimodal language decoding from brain activity: A review.

Brain research bulletin
Decoding brain activity is conducive to the breakthrough of brain-computer interface (BCI) technology. The development of artificial intelligence (AI) continually promotes the progress of brain language decoding technology. Existent research has main...

How can social robot use cases in healthcare be pushed - with an interoperable programming interface.

BMC medical informatics and decision making
INTRODUCTION: Research into current robot middleware has revealed that most of them are either too complicated or outdated. These facts have motivated the development of a new middleware to meet the requirements of usability by non-experts. The propo...

Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) is a challenging task in human-computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance. Despite the promisin...

Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control.

Sensors (Basel, Switzerland)
Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces ...

A Speech Recognition Method Based on Domain-Specific Datasets and Confidence Decision Networks.

Sensors (Basel, Switzerland)
This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model par...

Block-level dependency syntax based model for end-to-end aspect-based sentiment analysis.

Neural networks : the official journal of the International Neural Network Society
End-to-End aspect-based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and identify their sentiment polarities. Although previous research has demonstrated that syntax knowledge can be beneficial for E2E-ABSA, standard syntax depe...

Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning.

IEEE journal of biomedical and health informatics
Stuttering is a neuro-developmental speech impairment characterized by uncontrolled utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is caused by the failure of speech sensorimotors. Due to its complex natur...