PAS-SE: Personalized Auxiliary-Sensor Speech Enhancement for Voice Pickup in Hearables

Mattes Ohlenbusch^1,2, Mikolaj Kegler¹, Marko Stamenovic¹

¹Bose Corporation, USA   ²Fraunhofer IDMT, Germany  

Abstract

Speech enhancement for voice pickup in hearables aims to improve the user’s voice by suppressing noise and interfering talkers, while maintaining own-voice quality. For single-channel methods, it is particularly challenging to distinguish the target from interfering talkers without additional context. In this paper, we compare two strategies to resolve this ambiguity: personalized speech enhancement (PSE), which uses enrollment utterances to represent the target, and auxiliary‑sensor speech enhancement (AS-SE), which uses in‑ear microphones as additional input. We evaluate the strategies on two public datasets, employing different auxiliary sensor arrays, to investigate their cross-dataset generalization. We propose training‑time augmentations to facilitate cross-dataset generalization of AS-SE systems. We also show that combining PSE and AS-SE (PAS-SE) provides complementary performance benefits, especially when enrollment speech is recorded with the in‑ear microphone. We further demonstrate that PAS-SE personalized with noisy in-ear enrollments maintains performance benefits over the AS-SE system.