This study proposes a new two-layer approach for spoken language translation. First, we develop translated examples and transform them into speech signals. Second, to properly retrieve a translated example by analyzing speech signals, we expand the translated example into two layers: an intention layer and an object layer. The intention layer is used to examine intention similarity between the speech input and the translated example. The object layer is used to identify the objective components of the examined intention. Experiments were conducted with the languages of Chinese and English. The results revealed that our proposed approach achieves about 86% and 76% understandable translation rate for Chinese-to English and English-to-Chinese translations, respectively. TWO-LAYER APPROACH FOR SPOKEN LANGUAGE TRANSLATION
With the growing of globalization, people now often meet and do business with those who speak different languages, on-demand spoken language translation (SLT) has become increasingly important (See JANUS 111 [I], Verbmobil , EUTRANS , and ATR-MATRIX ). Recently, an integrated architecture based on stochastic finite-state transducer (SFST) has been presented for SLT [3,5]. The SFST approach integrated three models in a single network where the search process takes place. The three models are Hidden Markov Models for the acoustic part, language models for the source language and finite state transducers for the transfer between the source and target language. The output of this search process is the target word sequence associated to the optimal path. Fig. 1 shows an example of the SFST approach. The source sentence ‘‘una habitacidn doble” can he translated to either “a double room” or “a room with two beds”. The most probable translation is the first one with probability of 0.09.