  We propose TRAMBA, a hybrid transformer and Mamba architecture for acoustic
and bone conduction speech enhancement, suitable for mobile and wearable
platforms. Bone conduction speech enhancement has been impractical to adopt in
mobile and wearable platforms for several reasons: (i) data collection is
labor-intensive, resulting in scarcity; (ii) there exists a performance gap
between state of-art models with memory footprints of hundreds of MBs and
methods better suited for resource-constrained systems. To adapt TRAMBA to
vibration-based sensing modalities, we pre-train TRAMBA with audio speech
datasets that are widely available. Then, users fine-tune with a small amount
of bone conduction data. TRAMBA outperforms state-of-art GANs by up to 7.3% in
PESQ and 1.8% in STOI, with an order of magnitude smaller memory footprint and
an inference speed up of up to 465 times. We integrate TRAMBA into real systems
and show that TRAMBA (i) improves battery life of wearables by up to 160% by
requiring less data sampling and transmission; (ii) generates higher quality
voice in noisy environments than over-the-air speech; (iii) requires a memory
footprint of less than 20.0 MB.
