ArticleSensors (Basel, Switzerland)2023
Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- SignBridge Bilingual Sign Language Avatar-Construction Principles and Experts Quality Assessment.Sensors (Basel, Switzerland) · 2026Article
- Bidirectional Kazakh Sign Language prosody-aware translation using computer vision and speech recognition techniques.Frontiers in artificial intelligence · 2026Article
- Unsupervised Clustering and Ensemble Learning for Classifying Lip Articulation in Fingerspelling.Sensors (Basel, Switzerland) · 2025Article
- Assessment and Improvement of Avatar-Based Learning System: From Linguistic Structure Alignment to Sentiment-Driven Expressions.Sensors (Basel, Switzerland) · 2025Article
- Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This article is devoted to solving the problem of converting sign language into a consistent text with intonation markup for subsequent voice synthesis of sign phrases by speech with intonation. The paper proposes an improved method of continuous recognition of sign language, the results of which are transmitted to a natural language processor based on analyzers of morphology, syntax, and semantics of the Kazakh language, including morphological inflection and the construction of an intonation model of simple sentences. This approach has significant practical and social significance, as it can lead to the development of technologies that will help people with disabilities to communicate and improve their quality of life. As a result of the cross-validation of the model, we obtained an average test accuracy of 0.97 and an average val_accuracy of 0.90 for model evaluation. We also identified 20 sentence structures of the Kazakh language with their intonational model.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.