Statistical Analysis of Doubletalk Detection for Calibration and Performance Evaluation

Doubletalk detection is an important part of a practical echo canceller implementation, but a difficult problem is calibrating the doubletalk detector for arbitrary environments and input signals. In this paper, it is shown that a statistical model of a doubletalk detection variable's probability density function (PDF) can be used to obtain an optimal detection threshold and expected detection performance curves. In particular, a statistical analysis of a recently proposed cross-correlation-based doubletalk detector is presented. The doubletalk detection variable is modeled in terms of its constituent parameter estimators, resulting in conditional PDFs in the absence and presence of doubletalk. These are used to obtain a signal-adaptive detection threshold for calibration, and to provide expected doubletalk detection probability. Simulations are presented comparing the theoretical and measured detection probability compared to a fixed detection threshold for speech input and doubletalk signals. The results indicate a close agreement with the proposed model for moderate-to-high levels of doubletalk