AccCall: Enhancing Real-time Phone Call Quality with Smartphone's Built-in Accelerometer

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Abstract

Speech enhancement can greatly improve the user experience during phone calls in low signal-to-noise ratio (SNR) scenarios. In this paper, we propose a low-cost, energy-efficient, and environment-independent speech enhancement system, namely AccCall, that improves phone call quality using the smartphone's built-in accelerometer. However, a significant gap remains between the underlying insight and its practical applications, as several critical challenges should be addressed, including efficiency of speech enhancement in cross-user scenario, adaptive system triggering to reduce energy consumption, and lightweight deployment for real-time processing. To this end, we first design Acc-Aided Network (AccNet), a cross-modal deep learning model inherently capable of cross-user generalization through three key components, including cross-modal fusion module, accelerometer-aided (abbreviated as acc-aided) mask generator, the unified loss function. Second, we adopt a machine learning-based approach instead of deep learning to achieve high accuracy in distinguishing call activity states followed by adaptive system triggering, ensuring lower energy consumption and efficient deployment on mobile platforms. Finally, we propose a knowledge-distillation-driven structured pruning framework that optimizes model efficiency while preserving performance. Extensive experiments with 20 participants have been conducted under a user-independent scenario. The results show that AccCall achieves excellent and reliable adaptive triggering performance, and enables substantial real-time improvements in SISDR, SISNR, STOI, PESQ, and WER, demonstrating the superiority of our system in enhancing speech quality and intelligibility for phone calls.

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