https://hdl.handle.net/10481/64352 2026-04-17T06:59:28Z 2026-04-17T06:59:28Z López-Espejo, Iván Peinado, Antonio M. Gomez, Angel M. Gonzalez, Jose A. https://hdl.handle.net/10481/110976 2026-02-13T11:37:55Z

Dual-Channel Spectral Weighting for Robust Speech Recognition in Mobile Devices López-Espejo, Iván; Peinado, Antonio M.; Gomez, Angel M.; Gonzalez, Jose A.

López Espejo, Iván Roselló, Eros Edraki, Amin Harte, Naomi Jensen, Jesper https://hdl.handle.net/10481/97890 2024-12-11T10:16:37Z

Noise-Robust Hearing Aid Voice Control López Espejo, Iván; Roselló, Eros; Edraki, Amin; Harte, Naomi; Jensen, Jesper Advancing the design of robust hearing aid (HA) voice control is crucial to increase the HA use rate among hard of hearing people as well as to improve HA users’ experience. In this work, we contribute towards this goal by, first, presenting a novel HA speech dataset consisting of noisy own voice captured by 2 behind-the-ear (BTE) and 1 in-ear-canal (IEC) microphones. Second, we provide baseline HA voice control results from the evaluation of light, state-of-the-art keyword spotting mod- els utilizing different combinations of HA microphone signals. Experimental results show the benefits of exploiting bandwidth- limited bone-conducted speech (BCS) from the IEC microphone to achieve noise-robust HA voice control. Furthermore, results also demonstrate that voice control performance can be boosted by assisting BCS by the broader-bandwidth BTE microphone signals. Aiming at setting a baseline upon which the scientific community can continue to progress, the HA noisy speech dataset has been made publicly available.