语音增强与检测的多任务学习方法研究

在许多语音信号处理的实际应用中，都要求系统能够低延迟地实时处理多个任务，并且对噪声要有很强的鲁棒性。针对上述问题，提出了一种语音增强和语音活动检测（Voice Activity Detection，VAD）的多任务深度学习模型。该模型通过引入长短时记忆（Long Short-Term Memory，LSTM）网络，构建了一个适合于实时在线处理的因果系统。基于语音增强和VAD的强相关性，该模型以硬参数共享的方式连接了两个任务的输出层，不仅减少了计算量，还通过多任务学习提高了任务的泛化能力。实验结果表明，相较串行处理两个任务的基线模型，多任务模型在语音增强结果非常相近、VAD结果更优的情况下，其速度快了44.2%，这对于深度学习模型的实际应用和部署将具有重要的意义。

Multi-task Learning for Speech Enhancement and Detection

In many real-world applications of speech signal processing, real-time multi-task processing with low latency and strong robustness to noise is highly required. To solve the problem, a multi-task deep learning model of speech enhancement and Voice Activity Detection（VAD） is proposed. This model constructs a causal system suitable for real-time online processing by introducing a Long Short-Term Memory（LSTM） network. Based on the strong correlation between speech enhancement and VAD, the output layers of two tasks are connected using hard parameter sharing which lead a reduction of the number of parameters and an improvement of generalization ability of tasks through multi-task learning. Experimental results show that, processing speed of multi-task model improves considerably to 44.2% compared with the serial processing of baseline models with similar speech enhancement results and better VAD results, which is a great significance for the application and deployment of the deep learning model.