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Related Concept Videos

Genetic Lingo01:11

Genetic Lingo

Overview
Translation01:31

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Translation01:31

Translation

Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are called the...
Language01:16

Language

Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
Components of Language01:24

Components of Language

Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
Non-Verbal Cues01:29

Non-Verbal Cues

Non-verbal communication extends beyond gestures and facial expressions to include vocal elements known as paralanguage. Paralanguage consists of non-verbal vocal cues such as pitch, loudness, speech rate, pauses, and non-verbal vocalizations like laughter, sighs, and moans. These elements not only accompany speech but also provide critical emotional and contextual information.The Role of Paralanguage in CommunicationParalanguage adds depth to spoken language by conveying emotions and...

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Bengali-Sign: A Machine Learning-Based Bengali Sign Language Interpretation for Deaf and Non-Verbal People.

Md Johir Raihan1, Mainul Islam Labib1, Abdullah Al Jaid Jim1,2

  • 1Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

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|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study bridges the communication gap between deaf and hearing individuals by developing a convolutional neural network-squeeze excitation network for accurate sign language recognition. A smartphone app makes this advanced machine learning model accessible to everyone.

Keywords:
Bengali sign language (BdSL)SHAPconvolutional neural network (CNN)squeeze excitation (SE)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language is a primary communication method for deaf individuals, yet its use is limited among the hearing population.
  • A significant communication disparity persists between deaf and hearing communities.
  • Bridging this gap is crucial for fostering inclusivity and mutual understanding.

Purpose of the Study:

  • To develop an accurate machine learning model for sign language recognition.
  • To create a user-friendly smartphone application for accessing the sign language recognition model.
  • To enhance the interpretability of the machine learning model's predictions.

Main Methods:

  • A convolutional neural network-squeeze excitation network was developed for sign language sign prediction.
  • A smartphone application was created to deploy the machine learning model.
  • Shapley additive explanation (SHAP) was utilized for model interpretability.

Main Results:

  • The developed model achieved a high accuracy of 99.86% on the KU-BdSL dataset.
  • The squeeze-excitation (SE) block in the network improved model performance by focusing on relevant image channels.
  • SHAP analysis confirmed the model's reliance on hand-related visual cues, mirroring human sign language interpretation.

Conclusions:

  • The study successfully developed an accurate and accessible sign language recognition system.
  • The integration of SE blocks and SHAP analysis offers a robust approach to improving and understanding machine learning models in this domain.
  • The smartphone application democratizes access to sign language technology, promoting better communication between deaf and hearing individuals.