US Egypt Cooperative Research: Computer Aided Pronunciation Learning Application

这项合作研究项目由罗格斯大学的Ahmed Elgammal博士和埃及开罗大学的Sherif Abdou博士负责，目的是创建一个自动化系统，将语言学习者的发音沿着他们说话时面部的动态图像。 使用这些信息，所提出的系统将评估用户是否已经用目标语言产生了特定单词或短语的准确发音。 然后，该系统将向用户提供有关其发音质量的具体反馈，并提出改进建议。 具体来说，这项研究将需要分析英语和阿拉伯语的非母语者发生的发音错误。研究人员预计，这种语音识别系统将足够强大，以承受当用户使用非母语时可能发生的大的发音错误。 此外，系统将被设计为向用户分配通过或未通过分数？的话语，检测错误发生的地方，并分类错误，以便提供适当的反馈，如何发音应该改变。 为了创建这样一个系统，将开发新的算法，用于将视觉线索与音频线索相结合，以完成发音错误检测的任务。这将通过部署嘴唇跟踪算法和通过个人用户的风格相关模型研究嘴唇运动和语音之间的相关性来实现。这项研究计划还将导致收集一个按母语和非母语阿拉伯语和英语使用者分列的音素的大型视听数据库。 最初开发英语和阿拉伯语的发音错误检测的重要性在于，这两种语言都是语音丰富且彼此不同的，从而导致假设发音错误检测具有广泛的工作范围。 除了对语言学习的许多好处外，这项合作研究的预期成果是一种工具，它将成为任何计算机辅助语言学习（CALL）系统的核心组件之一。 这样的结果可以为听力受损的说话者提供具有成本效益和个性化的工具，其中获得可接受的发音可能具有挑战性。

This collaborative research project is being undertaken by Dr. Ahmed Elgammal, Rutgers University, and Dr. Sherif Abdou, Cairo University in Egypt, in order to create an automated system that will input pronunciations from language learners along with dynamic images of their faces as they speak. Using this information, the proposed system will assess whether the user has produced an accurate pronunciation of a particular word or phrase in the target language. The system will then offer specific feedback to the user regarding the quality of their pronunciation, with suggestions for improvement. Specifically, the research will entail analyzing pronunciation errors that occur for non-native speakers of English and Arabic.The researchers anticipate that this speech recognition system will be robust enough to withstand large mispronunciations that may occur when the user has a non-native tongue. Additionally, the system will be designed to assign a pass or fail score to the user?s utterance, detect where the error occurred, and classify the error in order to give adequate feedback on how the pronunciation should be altered. In order to create such a system, novel algorithms will be developed for combining visual cues with audio cues for the task of mispronunciation detection. This will be achieved by deploying lip tracking algorithms and studying the correlation between lip movement and speech through style-dependent models of individual users. The research plan will also result in collection of a large audio-visual database of the phonemes by native and non-native Arabic and English speakers. The significance of initially developing the mispronunciation detection for English and Arabic is that these two languages are both phonetically rich and different from one another, leading to an assumption that the mispronunciation detection has a wide working spectrum.The societal benefits of efficient speech training systems are significant. In addition to numerous benefits for language learning, the expected outcome of this collaborative research is a tool that would be one of the core components for any Computer Aided Language Learning (CALL) system. Such an outcome could provide a cost effective and personalized tool for hearing-impaired speakers where the acquisition of an acceptable pronunciation can be challenging.