手語是聽語障者主要之語言溝通方式，也可以說手語是他們所使用的母語。而常人與聽語障者之間的溝通與互動，均有賴能相互瞭解、語意清晰的手語，來突破彼此間的溝通障礙。然而，現階段的機器翻譯系統，架構大多停留在文字對應之層次，另一方面，手語之對照語料較為稀少且搜集不易，造成利用機器針對台灣手語進行翻譯之成效一直不甚理想。因此，本研究之目的即針對中文轉譯台灣手語之問題，提出一套轉譯機制作為解決方案，突破溝通障礙之限制，建立起常人對聽語障者之溝通管道。

　　本研究之架構，主要包含：1).中文詞庫之整合：根據不同中文詞庫之特性及所涵蓋之內容，進行詞庫合併與擴增之動作，建立一套完整之整合詞庫。2).語料之分析與相關資訊之擷取：針對手語轉譯機制所需之Context Free Grammar進行搜集之動作，同時利用EM演算法進行機率之訓練，以提供轉譯機制作翻譯機率之估算。3).中文轉手語翻譯機制之建立：以語言學之文句結構為基礎建立轉譯機制，利用樹狀結構轉換之方式，充分考量句法資訊與結構關係，並以完整之機率計算模型，涵蓋整個翻譯過程作信賴分數計算，提升翻譯架構之層次與翻譯成效。4).實驗設計與結果分析評估。

　　實驗中，選取2,036句對照語句(平均句長為5.6個詞)為訓練及測試語料，依此訓練共得7,931條轉換規則(Transfer Rule)。在轉譯效能評估部份，隨機選取80%對照語句為訓練語料，其餘則為測試語料。在AER評估方面，其評估結果為0.087；另外，其Top-1之翻譯正確率為81.6%，Top-5之正確率則為91.5%；最後，選取聽語障生與手語社學生總共20名，實際針對手語翻譯系統，分三等級評量其手語翻譯結果之成效，其平均翻譯成效為81%。本研究提出一套中文轉台灣手語翻譯系統，透過相關實驗之探討結果，顯示出本研究提出之轉譯機制在翻譯成效上之改善以及翻譯層次提升之可行性。

　　The hearing-impaired people generally use sign language to express their intention. However, hearing people don’t know how to use sign language and, therefore, the communication obstacle between them are formed. Presently, machine translation researches mainly focus on word-to-word translation, and some syntactic rule-based translation. On the other hand, the lack of parallel corpus of sign language limits the development of machine translation. For this reason, TSL translation system applied present machine translation technique will have no good performance. In this study, we propose a statistical approach using syntactic information for the translation from Chinese to Taiwanese Sign Language (TSL).

　　More specially, we focuses on 1) establishing a integrated corpus which consist of word, part of speech, semantic role, and semantic feature by combining information of several Chinese corpora, 2) collecting the context free grammar and training its probability by EM algorithm for proposal translation mechanism, 3) proposing a Chinese to Taiwanese Sign Language translation mechanism based on sentence structure and using syntactic information by complete statistical parsing model, and 4) integrating the above approaches into a Chinese to TSL translation system.

　　In order to evaluate our proposed approaches, 2,036 parallel sentences, in which the mean length of sentence is 5.6 words, were collected. Of this database, 80% was used as the training corpus and the remainder for testing, and 7,931 transfer rules were obtained. The translation performance achieved 81.6% and 91.5% accuracy for top-1 and top-5 candidates respectively, and got 0.087 Alignment Error Rate (AER). All of the above TSL translation　evaluations, our proposed approach achieved higher performance than IBM Model 3. In Mean Opinion Score evaluation, the average translation performance of our proposed system also achieved 81% satisfactory degree. Consequently, our proposed system can provide a channel of communication between the deaf and the able-bodied, and applied to TSL education in future.

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