An information filter for voice prompt suppression

Modern speech enabled applications provide for dialog between a machine and one or more human users. The machine prompts the user with queries that are either prerecorded or synthesized on the fly. The human users respond with their own voices, and their speech is then recognized and understood by a human language understanding module. In order to achieve as natural an interaction as possible, the human user(s) must be allowed to interrupt the machine during a voice prompt. In this work, we compare two techniques for such voice prompt suppression. The first is a straightforward adaptation of a conventional Kalman filter, which has certain advantages over the normalized least squares algrithm in terms of robustness and speed of convergence. The second algorithm, which is novel in this work, is also based on a Kalman filter, but differs from the first in that the update or correction step is performed in information space and hence allows for the use of diagonal loading in order to control the growth of the subband filter coefficients, and thereby add robustness to the VPS.