语音识别系统中上下文相关声学模型建模优化

在实验中发现，某些带调三音子的训练数据稀疏会引起识别错误率的上升,为了在一定程度上减少这种影响，提出了使用其无调三音子的模型参数对有调三音子进行初始化。此外还调整了决策树状态捆绑的停止门限，并且采用了混合度分量的自适应增长训练。在863语音库上的实验结果表明，所有这些获得了一定的音子识别性能提高，同时也一定程度上压缩了声学模型大小。

Refining Context-Dependent Tonal Acoustic Modeling in Mandarin LVCSR

In order to minimize the recognition errors caused by inaccurate model estimations from those toned triphones with limited training samples, we proposed to initialize toned triphones using their own toneless triphone model parameters. Besides, works concerning stopping criteria of decision tree state tying as well as mixture component adaptation are also explored to obtain better performance as well as reduce model scale. Experiments results have shown that, on the 863 corpus, along with all this improvements our system achieves certain increase of phone recognition rate, with much more trainable model scale as well.