An estimation of the keyword false acceptance error of automatic speech recognition systems as a function of their acoustic similarity to non-keywords
The process of the accuracy estimation of different automatic speech keyword spotting systems is very important for the developers. The most widespread estimation is a false error rate. This one highly depends on acoustic similarity of keywords to non-keywords in the certain test speech dataset. This paper proposes an approach which allows to reduce this drawback.