Dudy Shiran, Bedrick Steven, Asgari Meysam, Kain Alexander
Center for Spoken Language Understanding, Oregon Health & Science University, 3181 SW Sam Jackson Park Road Portland.
Comput Speech Lang. 2018 Jul;50:62-84. doi: 10.1016/j.csl.2017.12.006. Epub 2017 Dec 27.
Computer-Assisted Pronunciation Training (CAPT) systems aim to help a child learn the correct pronunciations of words. However, while there are many online commercial CAPT apps, there is no consensus among Speech Language Therapists (SLPs) or non-professionals about which CAPT systems, if any, work well. The prevailing assumption is that practicing with such programs is less reliable and thus does not provide the feedback necessary to allow children to improve their performance. The most common method for assessing pronunciation performance is the Goodness of Pronunciation (GOP) technique. Our paper proposes two new GOP techniques. We have found that pronunciation models that use explicit knowledge about error pronunciation patterns can lead to more accurate classification whether a phoneme was correctly pronounced or not. We evaluate the proposed pronunciation assessment methods against a baseline state of the art GOP approach, and show that the proposed techniques lead to classification performance that is more similar to that of a human expert.
计算机辅助发音训练（CAPT）系统旨在帮助儿童学习单词的正确发音。然而，虽然有许多在线商业CAPT应用程序，但言语语言治疗师（SLP）或非专业人士对于哪些CAPT系统（如果有的话）效果良好并没有达成共识。普遍的假设是，使用此类程序进行练习不太可靠，因此无法提供让儿童提高表现所需的反馈。评估发音表现最常用的方法是发音优度（GOP）技术。我们的论文提出了两种新的GOP技术。我们发现，使用关于错误发音模式的明确知识的发音模型可以导致更准确的分类，无论音素是否发音正确。我们将提出的发音评估方法与最先进的GOP基线方法进行了评估，并表明所提出的技术导致的分类性能更类似于人类专家的分类性能。
