Robustness aspects of active learning for acoustic modeling
Robustness aspects of active learning for acoustic modeling
复制标题
声学建模主动学习的稳健性
DOI：
10.21437/interspeech.2004-607
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
T. Kamm
中科院分区：
文献类型：
--
作者：
G. Meyer;T. Kamm
我们之前提出了[1]一种迭代的单词选择性训练方法，可以在不影响系统性能的情况下经济有效地利用数据准备资源。我们继续这项工作，并调查我们的主动学习方法的鲁棒性相对于启动条件，并进一步提出了一个停止标准，支持我们的目标，有效地利用转录工作，同时最大限度地减少系统误差。特别是，我们证明了对七个初始条件的鲁棒性，表明我们可以选择大约20小时的训练数据，并实现8.6%至9.0%的错误率范围，而使用所有50小时的训练集时，错误率为10%。此外，我们给出的经验证据表明，我们提出的停止标准一般是一个很好的预测时，达到最小的错误率，证明了每个初始条件。
We previously proposed [1] an iterative word-selective training method to cost-effectively utilize data preparation resources without compromising system performance. We continue this work and investigate the robustness of our active learning approach with respect to the starting conditions and further propose a stopping criterion that supports our objective to make effective use of transcription effort while minimizing system error. In particular, we demonstrate robustness to seven initial conditions, showing that we can select around 20 hours of training data and achieve a range of error rates between 8.6% and 9.0%, compared to an error rate of 10% when using all 50 hours of the training set. Additionally, we give empirical evidence that our proposed stopping criterion is in general a good predictor of when the minimum error rate is achieved, demonstrated for each of the initial conditions.