论文标题
增强尾部神经网络用于实时自定义关键字发现
Boosting Tail Neural Network for Realtime Custom Keyword Spotting
论文作者
论文摘要
在本文中，我们提出了一个提升尾部神经网络（BTNN），以提高实时自定义关键字点（RCK）的性能，这仍然是用有限的计算资源要求强大的分类能力的工业挑战。受到脑科学的启发，即大脑仅部分激活神经模拟，而开发了许多机器学习算法来使用一批弱分类器来解决艰巨的问题，这些问题通常被证明是有效的。我们证明此方法对RCKS问题有帮助。提出的方法在唤醒率和虚假警报方面取得了更好的性能。 在我们的实验中，与仅使用一个强分类器的传统算法相比，它具有18 \％的相对改进。我们还指出，这种方法在未来的ASR探索中可能会很有希望。
In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving the performance of Realtime Custom Keyword Spotting (RCKS) that is still an industrial challenge for demanding powerful classification ability with limited computation resources. Inspired by Brain Science that a brain is only partly activated for a nerve simulation and numerous machine learning algorithms are developed to use a batch of weak classifiers to resolve arduous problems, which are often proved to be effective. We show that this method is helpful to the RCKS problem. The proposed approach achieve better performances in terms of wakeup rate and false alarm. In our experiments compared with those traditional algorithms that use only one strong classifier, it gets 18\% relative improvement. We also point out that this approach may be promising in future ASR exploration.
