A New Class of Doubletalk Detectors Based on Cross-Correlation

A doubletalk detector (DTD) is used with an echo canceler to sense when far-end speech is corrupted by near-end speech. Its role is to freeze the adaptation of the model filter when near-end speech is present, in order to avoid divergence of the adaptive algorithm. Several authors have proposed to use the cross-correlation coefficient vector between the input signal vector x and the scalar output y for a DTD. We show in this paper that this measure is not appropriate and propose a modified form that meets, in an optimal way, the needs for an efficient DTD. By extension, we also propose a definition of the normalized cross-correlation matrix between two vectors and show a link with the coherence function.