TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks

• URL: http://arxiv.org/abs/2505.07890v4
• Date: Wed, 18 Jun 2025 07:55:43 GMT
• Title: TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks
• Authors: Kutay Ertürk, Furkan Altınışık, İrem Sarıaltın, Ömer Nezih Gerek,
• Abstract summary: TSLFormer treats sign gestures as ordered, string-like language.<n>Method only works with 3D joint positions extracted from Google's Mediapipe library.<n>Results show that joint-based input is sufficient for enabling real-time, mobile, and assistive communication systems for hearing-impaired individuals.
• Score: 0.3749861135832072
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study presents TSLFormer, a light and robust word-level Turkish Sign Language (TSL) recognition model that treats sign gestures as ordered, string-like language. Instead of using raw RGB or depth videos, our method only works with 3D joint positions - articulation points - extracted using Google's Mediapipe library, which focuses on the hand and torso skeletal locations. This creates efficient input dimensionality reduction while preserving important semantic gesture information. Our approach revisits sign language recognition as sequence-to-sequence translation, inspired by the linguistic nature of sign languages and the success of transformers in natural language processing. Since TSLFormer uses the self-attention mechanism, it effectively captures temporal co-occurrence within gesture sequences and highlights meaningful motion patterns as words unfold. Evaluated on the AUTSL dataset with over 36,000 samples and 227 different words, TSLFormer achieves competitive performance with minimal computational cost. These results show that joint-based input is sufficient for enabling real-time, mobile, and assistive communication systems for hearing-impaired individuals.

Related papers

• SSLR: A Semi-Supervised Learning Method for Isolated Sign Language Recognition [2.409285779772107]
  Sign language recognition systems aim to recognize sign gestures and translate them into spoken language.<n>One of the main challenges in SLR is the scarcity of annotated datasets.<n>We propose a semi-supervised learning approach for SLR, employing a pseudo-label method to annotate unlabeled samples.
  arXiv  Detail & Related papers  (2025-04-23T11:59:52Z)
• Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator [55.94334001112357]
  We introduce a multilingual sign language model, Signs as Tokens (SOKE), which can generate 3D sign avatars autoregressively from text inputs.<n>We propose a retrieval-enhanced SLG approach, which incorporates external sign dictionaries to provide accurate word-level signs.
  arXiv  Detail & Related papers  (2024-11-26T18:28:09Z)
• CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens [49.569695524535454]
  We propose to represent speech with supervised semantic tokens, which are derived from a multilingual speech recognition model by inserting vector quantization into the encoder. Based on the tokens, we further propose a scalable zero-shot TTS synthesizer, CosyVoice, which consists of an LLM for text-to-token generation and a conditional flow matching model for token-to-speech synthesis.
  arXiv  Detail & Related papers  (2024-07-07T15:16:19Z)
• SignSpeak: Open-Source Time Series Classification for ASL Translation [0.12499537119440243]
  We propose a low-cost, real-time ASL-to-speech translation glove and an exhaustive training dataset of sign language patterns. We benchmarked this dataset with supervised learning models, such as LSTMs, GRUs and Transformers, where our best model achieved 92% accuracy. Our open-source dataset, models and glove designs provide an accurate and efficient ASL translator while maintaining cost-effectiveness.
  arXiv  Detail & Related papers  (2024-06-27T17:58:54Z)
• Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation [3.976851945232775]
  Current approaches for sign language recognition rely on RGB video inputs, which are vulnerable to fluctuations in the background. We propose a multi-stream keypoint attention network to depict a sequence of keypoints produced by a readily available keypoint estimator. We carry out comprehensive experiments on well-known benchmarks like Phoenix-2014, Phoenix-2014T, and CSL-Daily to showcase the efficacy of our methodology.
  arXiv  Detail & Related papers  (2024-05-09T10:58:37Z)
• Neural Sign Actors: A diffusion model for 3D sign language production from text [51.81647203840081]
  Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities. This work makes an important step towards realistic neural sign avatars, bridging the communication gap between Deaf and hearing communities.
  arXiv  Detail & Related papers  (2023-12-05T12:04:34Z)
• Fine-tuning of sign language recognition models: a technical report [0.0]
  We focus on investigating two questions: how fine-tuning on datasets from other sign languages helps improve sign recognition quality, and whether sign recognition is possible in real-time without using GPU. We provide code for reproducing model training experiments, converting models to ONNX format, and inference for real-time gesture recognition.
  arXiv  Detail & Related papers  (2023-02-15T14:36:18Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble [71.97020373520922]
  Sign language is commonly used by deaf or mute people to communicate. We propose a novel Multi-modal Framework with a Global Ensemble Model (GEM) for isolated Sign Language Recognition ( SLR) Our proposed SAM- SLR-v2 framework is exceedingly effective and achieves state-of-the-art performance with significant margins.
  arXiv  Detail & Related papers  (2021-10-12T16:57:18Z)
• Skeleton Based Sign Language Recognition Using Whole-body Keypoints [71.97020373520922]
  Sign language is used by deaf or speech impaired people to communicate. Skeleton-based recognition is becoming popular that it can be further ensembled with RGB-D based method to achieve state-of-the-art performance. Inspired by the recent development of whole-body pose estimation citejin 2020whole, we propose recognizing sign language based on the whole-body key points and features.
  arXiv  Detail & Related papers  (2021-03-16T03:38:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.