A robust endpoint detector based on differential parameters and fuzzy pattern recognition

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