UltraSE

Robust speech enhancement is considered as the holy grail of audio processing and a key requirement for human-human and humanmachine interaction. Solving this task with single-channel, audioonly methods remains an open challenge, especially for practical scenarios involving a mixture of competing speakers and background noise. In this paper, we propose UltraSE, which uses ultrasound sensing as a complementary modality to separate the desired speaker's voice from interferences and noise. UltraSE uses a commodity mobile device (e.g., smartphone) to emit ultrasound and capture the reflections from the speaker's articulatory gestures. It introduces a multi-modal, multi-domain deep learning framework to fuse the ultrasonic Doppler features and the audible speech spectrogram. Furthermore, it employs an adversarially trained discriminator, based on a cross-modal similarity measurement network, to learn the correlation between the two heterogeneous feature modalities. Our experiments verify that UltraSE simultaneously improves speech intelligibility and quality, and outperforms state-of-the-art solutions by a large margin.