基于深度神经网络的多任务学习改进音素识别

M. Seltzer, J. Droppo
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引用次数: 244

摘要

在本文中,我们演示了如何使用多任务学习来提高深度神经网络(DNN)声学模型的性能。在多任务学习中,网络被训练成使用共享表示来执行主要分类任务和一个或多个次要任务。与次要任务相关联的附加模型参数表示训练参数数量的非常小的增加,并且可以在运行时丢弃。在本文中,我们探讨了辅助任务的三种自然选择:电话标签、电话上下文和状态上下文。我们证明,即使在较强的基线上,多任务学习也能显著降低错误率。使用电话上下文,TIMIT上的语音错误率(PER)在核心测试集中从21.63%降低到20.25%,并且超过了使用标准前馈网络架构的DNN的最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-task learning in deep neural networks for improved phoneme recognition
In this paper we demonstrate how to improve the performance of deep neural network (DNN) acoustic models using multi-task learning. In multi-task learning, the network is trained to perform both the primary classification task and one or more secondary tasks using a shared representation. The additional model parameters associated with the secondary tasks represent a very small increase in the number of trained parameters, and can be discarded at runtime. In this paper, we explore three natural choices for the secondary task: the phone label, the phone context, and the state context. We demonstrate that, even on a strong baseline, multi-task learning can provide a significant decrease in error rate. Using phone context, the phonetic error rate (PER) on TIMIT is reduced from 21.63% to 20.25% on the core test set, and surpassing the best performance in the literature for a DNN that uses a standard feed-forward network architecture.
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