DOUBLETALK DETECTION USING REAL TIME RECURRENT LEARNING

In this paper we present a new system for doubletalk detection that uses multiple signal detectors/discriminators based on recurrent networks. The goal is to build a simple system that learns to combine information from different signal sources to make robust decisions even under changing noise conditions. In this paper we use three detectors - two of these are frequency domain signal detectors, one at the far-end and one at the microphone channel. The third detector determines the relative level of nearend speech vs. far-end echo in the microphone signal. The new doubletalk detector combines information from all these detectors to make its decision. An important part of this proposed design is that the features used by these detectors can be easily tracked online in the presence of noise. We compare our results with cross-correlation based doubletalk detectors to show its effectiveness.

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