LargeCall: Large-Model-Assisted Phone Call Enhancement Using Smartphone's Built-in Accelerometer

Achieving intelligible and noise-robust voice communication during phone calls remains a significant challenge in real-world environments, where low signal-to-noise ratio (SNR) and background conversations are prevalent. While traditional audio-only speech enhancement systems show strong performance under moderate noise, they struggle in multi-speaker or low SNR scenarios due to the lack of user-specific cues. In this work, we propose LargeCall, a dual-modality speech enhancement framework that integrates microphone audio with inertial signals from the smartphone’s built-in accelerometer. Unlike visual or ultrasound-based methods, accelerometer sensing is passive, privacy-preserving, and robust in outdoor settings. Our design addresses three key challenges: (i) the scarcity of paired audio-accelerometer data is mitigated by bootstrapping from a pretrained audio-only encoder, reducing training data requirements and improving generalization; (ii) variability in phone orientation and body motion is handled through a posture-invariant transformation pipeline and a multi-dilated fusion module for robust feature extraction; and (iii) modality mismatch between audio and accelerometer streams is resolved via a cross-modal mask fusion strategy, which adaptively integrates complementary masks from both modalities without disrupting the pretrained encoder. We validate LargeCall on a cross-user evaluation protocol using realistic phone call scenarios. Experimental results show that LargeCall achieves substantial gains in both objective and subjective metrics across diverse noise conditions, demonstrating its effectiveness and real-world deployment potential. The demo of our system is available at https://anonymoususers718.github.io/largecall/.