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A Single-End-Point DTW Algorithm for Keyword Spotting  

최용선 (한국과학기술원 바이오시스템학과 및 뇌과학연구센터)
오상훈 (목원대학교 정보통신전파공학)
이수영 (한국과학기술원 바이오시스템학과 및 뇌과학연구센터)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.41, no.3, 2004 , pp. 209-219 More about this Journal
Abstract
In order to implement a real time hardware for keyword spotting, we propose a Single-End-Point DTW(SEP-DTW) algorithm which is simple and less complex for computation. The SEP-DTW algorithm only needs a single end point which enables efficient applications, and it has a small wont of computations because the global search area is divided into successive local search areas. Also, we adopt new local constraints and a new distance measure for a better performance of the SEP-DTW algorithm. Besides, we make a normalization of feature same vectors so that they have the same variance in each frequency bin, and each frame has the same energy levels. To construct several reference patterns for each keyword, we use a clustering algorithm for all training patterns, and mean vectors in every cluster are taken as reference patterns. In order to detect a key word for input streams of speech, we measure the distances between reference patterns and input pattern, and we make a decision whether the distances are smaller than a pre-defined threshold value. With isolated speech recognition and keyword spotting experiments, we verify that the proposed algorithm has a better performance than other methods.
Keywords
DTW;
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