Modified energy based method for word endpoints detection of continuous speech signal in real world environment

Accurately identifying the word endpoints is an important step of speech recognition process. This paper proposes a robust word endpoints detection algorithm of continuous speech signal collected from real world environment. In this process energy feature is used along with zero crossing rate feature to locate the endpoints of word in speech signal. A set of 100 different sentences have been recorded from 10 speakers which are used as referred dataset. Proposed method is applied on that dataset and accuracy has been measured by computing the average difference between ground truth endpoints (manually estimated) and system generated endpoints. This algorithm attains 85.5% accuracy whereas the entropy based method gives the accuracy of 78.6 % which shows the superiority of the proposed method. Euclidean distance and Manhattan distance for the proposed algorithm is 2.4 and 2.8 respectively which is also quite acceptable.