# A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions

arXiv: https://arxiv.org/abs/2508.07561 · HTML: https://mlsanity.com/paper/2508.07561/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://mlsanity.com/paper/2206.08317/) (2022)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [Deep-FSMN for Large Vocabulary Continuous Speech Recognition](https://mlsanity.com/paper/1803.05030/) (2018)
- [Data augmentation and loss normalization for deep noise suppression](https://mlsanity.com/paper/2008.06412/) (2020)
- [Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline](https://mlsanity.com/paper/1803.10109/) (2018)
- [Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction](https://mlsanity.com/paper/2305.12450/) (2023)
- [NN3A: Neural Network supported Acoustic Echo Cancellation, Noise Suppression and Automatic Gain Control for Real-Time Communications](https://mlsanity.com/paper/2110.08437/) (2021)
- [A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement](https://mlsanity.com/paper/2102.07330/) (2021)
- [Multi-Task Deep Residual Echo Suppression with Echo-aware Loss](https://mlsanity.com/paper/2202.06850/) (2022)

## Abstract (arXiv)

In full-duplex speech interaction systems, effective Acoustic Echo Cancellation (AEC) is crucial for recovering echo-contaminated speech. This paper presents a neural network-based AEC solution to address challenges in mobile scenarios with varying hardware, nonlinear distortions and long latency. We first incorporate diverse data augmentation strategies to enhance the model's robustness across various environments. Moreover, progressive learning is employed to incrementally improve AEC effectiveness, resulting in a considerable improvement in speech quality. To further optimize AEC's downstream applications, we introduce a novel post-processing strategy employing tailored parameters designed specifically for tasks such as Voice Activity Detection (VAD) and Automatic Speech Recognition (ASR), thus enhancing their overall efficacy. Finally, our method employs a small-footprint model with streaming inference, enabling seamless deployment on mobile devices. Empirical results demonstrate effectiveness of the proposed method in Echo Return Loss Enhancement and Perceptual Evaluation of Speech Quality, alongside significant improvements in both VAD and ASR results.