融合自然語言處理與 Transformer 模型之中文至自然手語翻譯系統;A Chinese-to-Natural Sign Language Translation System Integrating NLP and Transformer Models

NCU Institutional Repository > 資訊電機學院 > 資訊工程研究所 > 博碩士論文 > Item 987654321/98504

請使用永久網址來引用或連結此文件: https://ir.lib.ncu.edu.tw/handle/987654321/98504

題名:	融合自然語言處理與 Transformer 模型之中文至自然手語翻譯系統;A Chinese-to-Natural Sign Language Translation System Integrating NLP and Transformer Models
作者:	朱禹丞;Chu, Yu-Cheng
貢獻者:	資訊工程學系
關鍵詞:	中文轉換;手語翻譯;自然手語;自然語言處理;Transformer;Chinese-to-Sign Translation;Sign Language Translation;Natural Sign Language;Natural Language Processing;Transformer
日期:	2025-08-04
上傳時間:	2025-10-17 12:51:26 (UTC+8)
出版者:	國立中央大學
摘要:	根據2024年底衛福部統計資料顯示，台灣登記在案的聽覺機能障礙者（Hearing Impairment）人數已達138,966人。這些聽障人士在日常生活中，主要依賴台灣自然手語（Taiwan Sign Language, TSL）進行溝通。然而，一般民眾在學習手語時，通常是透過手語書或課程學習文法式、結構化的手語，其語言表達方式與自然手語差異甚大，造成聽障人士與聽人的溝通往往出現理解困難與落差。提升一般人對自然手語的理解與應用，有助於促進更順暢的雙向溝通，進而實踐資訊無障礙的理念。為此，本文提出一種將書面中文自動轉換為自然手語的系統，協助聽人能更有效地表達出聽障人士熟悉的自然手語句型。系統採用基於 Transformer 架構的深度學習方法，透過平行語料訓練模型，自動學習中文與自然手語之間的對應關係。此外，本研究結合自然語言處理（NLP）工具，對輸入中文句進行詞性標註、依存句法分析、成分句法分析與斷詞處理，以協助模型掌握語法與語意特徵，提升轉換品質。實驗結果顯示，本系統能有效處理多種中文語句類型，並能正確調整語序與詞彙，生成貼近自然手語表達邏輯的輸出內容。此方法未來可應用於手語教學輔助系統、跨語言溝通平台或公共服務場域，具備實質的社會應用潛力與推廣價值。;According to statistics from Taiwan′s Ministry of Health and Welfare at the end of 2024, there are 138,966 registered individuals with hearing impairments. In their daily lives, these individuals primarily rely on Taiwan Sign Language (TSL) for communication. However, most hearing individuals learn sign language through books or structured courses, which are based on formalized and grammatical versions of sign language that differ significantly from natural TSL. This discrepancy often leads to communication gaps and misunderstandings between hearing and deaf individuals. Enhancing the general public’s understanding and use of natural sign language can facilitate smoother two-way communication and promote the concept of information accessibility. To address this issue, this study proposes a system that automatically translates written Mandarin Chinese into natural TSL expressions, helping hearing individuals convey messages in a form more familiar to the deaf community. The system employs a deep learning approach based on the Transformer architecture, learning the mapping between Mandarin and natural sign language through parallel corpora. Additionally, natural language processing (NLP) tools are integrated to perform part-of-speech tagging, dependency parsing, constituency parsing, and word segmentation on the input text, enabling the model to better capture syntactic and semantic features and improve translation quality. Experimental results show that the system can effectively handle various types of Chinese sentences, accurately adjust word order and vocabulary, and generate output aligned with the logic of natural TSL expression. This approach holds practical potential for applications such as sign language education tools, cross-linguistic communication platforms, and public service systems.
顯示於類別:	[資訊工程研究所] 博碩士論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	4	檢視/開啟

在NCUIR中所有的資料項目都受到原著作權保護.

社群 sharing

資料載入中.....