Neural Network for Speech and Gesture Semantics

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurolinguistics".

Deadline for manuscript submissions: closed (6 January 2024) | Viewed by 2060

Special Issue Editors


E-Mail Website
Guest Editor
Institute of Neuroscience, National Research Council, 43125 Parma, Italy
Interests: embodied cognition; motor performance; language; gesture

E-Mail Website
Guest Editor
1. Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
2. Child and Adolescent Neuropsychiatry-NPIA District of Scandiano, AUSL of Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: cognitive neuroscience; posture; motor activity; emotion recognition

Special Issue Information

Dear Colleagues,

The recent embodied view of cognition is open to relevant claims about a motor origin of language function, whereby it is proposed to have gradually evolved from a communication system initially based on manual gestures. Today, the use of gestures remains a universal feature of human communication, accompanying spoken language with the function of integrating the message semantics.

Many studies in the last years support this idea, offering evidence that gesture and speech semantics might be processed in the same brain areas, with overlapping activation of sensorimotor and linguistic systems. However, the current evidence is far from conclusive regarding the definition of a shared neural network and is not definitive about the essentiality of motor system contribution.

This Special Issue will highlight empirical and theoretical advances in investigating the neural basis of semantic processing and integration of gestural and linguistic signals during their comprehension and production. The aim is to offer a coherent framework of the involved brain circuitry and to collect data in supporting or contradicting recent theories of embodied cognition and the motor origin of language.

Original research articles in addition to reviews and theoretical contributions are welcome. The use of diverse methods from cognitive neuroscience, including functional MRI, scalp, or intracranial EEG and TMS, also integrated with electromyography and movement analysis techniques, are encouraged.

Dr. Doriana De Marco
Dr. Elisa De Stefani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • embodied cognition
  • language
  • gestures
  • semantics
  • speech

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 8438 KiB  
Article
A Semi-Supervised Speech Deception Detection Algorithm Combining Acoustic Statistical Features and Time-Frequency Two-Dimensional Features
by Hongliang Fu, Hang Yu, Xuemei Wang, Xiangying Lu and Chunhua Zhu
Brain Sci. 2023, 13(5), 725; https://doi.org/10.3390/brainsci13050725 - 26 Apr 2023
Cited by 2 | Viewed by 1501
Abstract
Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the [...] Read more.
Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the generalization ability of the widely used semi-supervised speech deception detection model worse. Because of this, this paper proposes a semi-supervised speech deception detection algorithm combining acoustic statistical features and time-frequency two-dimensional features. Firstly, a hybrid semi-supervised neural network based on a semi-supervised autoencoder network (AE) and a mean-teacher network is established. Secondly, the static artificial statistical features are input into the semi-supervised AE to extract more robust advanced features, and the three-dimensional (3D) mel-spectrum features are input into the mean-teacher network to obtain features rich in time-frequency two-dimensional information. Finally, a consistency regularization method is introduced after feature fusion, effectively reducing the occurrence of over-fitting and improving the generalization ability of the model. This paper carries out experiments on the self-built corpus for deception detection. The experimental results show that the highest recognition accuracy of the algorithm proposed in this paper is 68.62% which is 1.2% higher than the baseline system and effectively improves the detection accuracy. Full article
(This article belongs to the Special Issue Neural Network for Speech and Gesture Semantics)
Show Figures

Figure 1

Back to TopTop