This paper proposes a method for extracting the correct parts from speech recognition results by using an example-based approach for parsing those results that include several recognition errors. Correct parts are extracted using two factors:(1)the semantic distance between the input expression and example expression,and(2)the structure selected by the shortest semantic distance. The examination results showed that the proposed method is able to efficiently extract the correct parts from speech recognition results. About ninety-six percent of the extracted parts are correct.The results also showed that the proposed method is effective in understanding misrecognition speech sentences and in improving speech translation results.The misunderstanding rate for erroneous sentences is reduced about half.Sixty-nine percent of speech translation results are improved for misrecognized sentences.
In this paper, we describe an experimental speech translation system utilizing small, PC-based hardware with multi-modal user interface. Two major problems for people using an automatic speech translation device are speech recognition errors and language translation errors. In this paper we focus on developing techniques to overcome these problems. The techniques include a new language translation approach based on example sentences, simplified expression rules, and a multi-modal user interface which shows possible speech recognition candidates retrieved from the example sentences. Combination of the proposed techniques can provide accurate language translation performance even if the speech recognition result contains some errors. We propose to use keyword classes by looking at the dependency between keywords to detect the misrecognized keywords and to search the example expressions. Then, the suitable example expression is chosen using a touch panel or by pushing buttons. The language translation picks up the expression in the other language, which should always be grammatically correct. Simplified translated expressions are realized by speech-act based simplifying rules so that the system can avoid various redundant expressions. A simple comparison study showed that the proposed method outputs almost 2 to 10 times faster than a conventional translation device.
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