The impact of speech detection errors on the noise reduction performance of multi-channel Wiener filtering and Generalized Sidelobe Cancellation

In contrast to the Generalized Sidelobe Canceller (GSC), the noise reduction performance of a recently developed multi-channel Wiener filter (MWF) technique does not depend on the validity of a priori assumptions about the signal model. This provides a potential benefit of the MWF over the GSC. However, both techniques also rely on a speech detection algorithm. In this paper, we analyze the average effect of speech detection errors on the performance of the GSC and the MWF both theoretically and experimentally. In the GSC case, it is the simultaneous presence of signal model errors and speech detection errors that affects performance. Incorporating a constraint on the noise sensitivity of the GSC limits the drastic impact of speech detection errors at the expense of reduced noise reduction performance. It is shown that the MWF preserves its benefit over the GSC for a reasonable speech detection error rate of 20% or less, even when the GSC is supplied with a noise sensitivity constraint. Real data experiments confirm the theoretical results.