Echo Detection and Delay Estimation using a Pattern Recogntion Approach and Cepstral Correlation

In this paper we present a method for echo detection and echo path delay estimation using a pattern recognition approach. We consider the problem of echo detection as attempting to match a speech pattern in the near-end signal to the far-end signal at a given delay. Employing features and techniques that have been successfully used in speech recognition, we define a spectral similarity function based on cepstral correlation. We show, through experimental results, that the proposed similarity function can reliably detect acoustic echoes and correctly estimate the echo path delay. Further, it is shown that the similarity function can be used in the detection of double-talk conditions. The method presented here is applicable to both electrical (hybrid) network echoes as well as to acoustic echoes.