一个模型搞定所有延迟需求
语音增强模型通常得为不同延迟场景单独训练：实时通话要低延迟，离线处理可以高延迟。这篇论文用一个模型解决了这个问题——它通过并行卷积层让算法延迟可调，又用早退机制控制计算延迟，训练时还用了两阶段策略来缩小灵活模型和专用模型的性能差距。结果是一个模型就能覆盖从低延迟到高延迟的各种场景，不用再为每个场景重新训练。这不是你明天能直接用的工具，但它展示了AI模型从“专用”走向“通用”的一个具体路径。
📄 原文摘要(英文)
Different real-time speech applications impose distinct latency budgets, often requiring separately trained enhancement models for each scenario. In this paper, we propose a one-for-all, real-time universal speech enhancement model that provides explicit control over both algorithmic and computational latency. Algorithmic latency is flexibly adjusted via configurable look-ahead frames. To avoid learning inefficiency caused by varying padding configurations, we introduce parallel convolutional layers corresponding to different look-ahead settings. Computational latency is controlled through an early-exit mechanism, enabling inference at different network depths. To narrow the performance gap between specialized and flexible models, we propose a two-stage training strategy with a shared-to-multiple decoder transition. Overall, the proposed framework enables a single model to be deployed across diverse latency budgets without retraining separate models.