Robust word boundary detection using fuzzy logic

A pattern recognition approach to robust word boundary detection in adverse acoustic noise conditions is proposed. The algorithm uses four simple differential parameters calculated in the time domain and pattern matching based on a set of six fuzzy rules extracted by a hybrid learning tool. The experimental results demonstrate that the new endpoint detector outperforms traditional methods, in particular in the presence of high levels of background noise