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A Recursive Dialogue Game for Personalized Computer-Aided Pronunciation Training

机译:个性化计算机辅助语音训练的递归对话游戏

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Learning languages in addition to the native language is very important for all people in the globalized world today, and computer-aided pronunciation training (CAPT) is attractive since the software can be used anywhere at any time, and repeated as many times as desired. In this paper, we introduce the immersive interaction scenario offered by spoken dialogues to CAPT by proposing a recursive dialogue game to make CAPT personalized. A number of tree-structured sub-dialogues are linked sequentially and recursively as the script for the game. The system policy at each dialogue turn is to select in real-time along the dialogue the best training sentence for each specific individual learner within the dialogue script, considering the learner’s learning status and the future possible dialogue paths in the script, such that the learner can have the scores for all pronunciation units considered reaching a predefined standard in a minimum number of turns. The purpose here is that those pronunciation units poorly produced by the specific learner can be offered with more practice opportunities in the future sentences along the dialogue, which enables the learner to improve the pronunciation without having to repeat the same training sentences many times. This makes the learning process for each learner completely personalized. The dialogue policy is modeled by Markov decision process (MDP) with high-dimensional continuous state space, and trained with fitted value iteration using a huge number of simulated learners. These simulated leaners have the behavior similar to real learners, and were generated from a corpus of real learner data. The experiments demonstrated very promising results and a real cloud-based system is also successfully implemented.
机译:对于当今世界的所有人来说,除了母语之外,学习语言也非常重要,并且计算机辅助发音训练(CAPT)具有吸引力,因为该软件可随时随地使用,并可根据需要重复多次。在本文中,我们通过提出使CAPT个性化的递归对话游戏,向CAPT介绍了口语对话提供的沉浸式交互场景。许多树状子对话被顺序和递归链接为游戏脚本。在每个对话轮次时,系统策略是在对话过程中实时考虑对话脚本中每个特定学习者的最佳训练句子,同时考虑学习者的学习状态和脚本中未来可能的对话路径,从而使学习者可以让所有发音单元的分数在最少的回合时间内达到预定义的标准。此处的目的是,在将来的对话中,可以为特定学习者产生的发音单元的发音不佳提供更多的练习机会,这使学习者无需多次重复相同的训练句子即可提高发音。这使得每个学习者的学习过程完全个性化。对话策略由具有高维连续状态空间的马尔可夫决策过程(MDP)建模,并使用大量模拟学习者进行了拟合值迭代训练。这些模拟的学习者具有与真实学习者相似的行为,并且是根据真实学习者数据的语料库生成的。实验证明了非常有希望的结果,并且还成功地实现了基于云的真实系统。

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