Online bone/air-conducted speech fusion in the presence of strong narrowband noise

Abstract

Online adaptive bone/air-conducted speech fusion is a very promising and attractive technique for real time speech communication in noisy environments. This paper firstly demonstrates that conventional bone/air-conducted speech fusion systems may degrade significantly, despite that only a tiny narrowband noise pollution persists in bone-conducted speech by extensive simulations and experiments. To address this issue, a robust nonlinear system is proposed that successfully enhances the speech contaminated by the harsh narrowband noise. The penetrated narrowband noise is extracted by an adaptive linear prediction filter from an extra reference signal that is capture by a non-contacted bone sensor, and then fed to the parallel adaptive noise cancellers to reconstruct a cleaner error feedback for the speech recovery system. Due to this combination, the proposed system enjoys both attractive robustness against the harsh additive noise and strong real-time capability. Simulations as well as application to a real factory noise are conducted to demonstrate the superior performance of the proposed system.