Dual channel based speech enhancement using novelty filter for robust speech recognition in automobile environment

In this paper, a simple and effective dual channel speech enhancement algorithm is proposed. The proposed algorithm incorporates a novelty filter scheme, performing associative memory and aiming at improving the performance of the dual channel Generalized Sidelobe Canceller (GSC.) The novelty filter is demonstrated as effective for enhancing the reference noise used in the dual channel GSC, such that the assumptions required for adaptive noise cancellation are satisfied. In this paper, the concept of a novelty filter scheme is described, in addition to the conditions required to guarantee the performance of the dual channel GSC and the method of applying the novelty filter scheme to speech enhancement objectives. In representative experiments, superiority of the proposed algorithm is demonstrated using three objective measures; segmental Signal to Noise Ratio (SNR), Log Spectral Distance (LSD) and word accuracy, which are evaluated via speech data recorded in real automobile environments.