自动发音错误检测中基于F1值最大化的声学模型训练方法

摘要为了提高计算机辅助语言学习中自动发音错误检测系统的性能，提出一种声学模型的区分性训练方法。该方法将经过正确度标注的非母语语音数据库上的发音错误检测的F₁值的最大化作为模型参数的训练准则。采用Sigmoid 函数对F₁值函数进行平滑构造目标函数，并利用构造弱意义辅助函数的方法以及扩展Baum-Welch 形式的参数更新公式进行优化。提出在模型参数更新与音素门限同时优化的策略保证目标函数增长的单调性。发音错误检测实验表明该方法能够有效地增大训练和测试数据检错的F₁值。同时训练数据和测试数据上的精确度、召回率以及检测正确度都有明显改进。 

Maximum F1-score acoustic model training for automatic mispronunciation detection

To improve the performance of automatic mispronunciation detection in computer-assisted language learning, a discriminative acoustic model training method is proposed. The method aims at maximizing the F₁-score of mispronunciation detection results on the annotated non-native speech database. The training objective function is formulated as a smooth form of the F₁-score by using the sigmoid function, and is optimized by using the extended Baum-Welch form like updating equations based on the weak-sense auxiliary function method. Simultaneous updating strategy of acoustic models and phone threshold parameters is proposed to ensure monotonicity of the objective function improvement. Mispronunciation detection experiments show that the method is effective in increasing the F₁-score,precision, recall and detection accuracy on both the training and evaluation data set.