고립 단어 인식 결과의 비유사 후보 단어 제외 성능을 개선하기 위한 다양한 접근 방법 연구

Various Approaches to Improve Exclusion Performance of Non-similar Candidates from N-best Recognition Results on Isolated Word Recognition

Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. The previous study [1,2] investigated several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. This paper discusses the various improving techniques of removing non-similar recognition results. The mentioned methods include comparison penalties or weights, phone accuracy based on confusion information, weights candidates by ranking order and partial comparisons. Through experimental results, it is found that some proposed method keeps more accurate recognition results than the previous method's results.