使用PocketSphinx对齐和特征提取的英语口语可理解性补救在最新技术水平上有了实质性改进
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art
摘要：
我们使用自动语音识别来评估英语口语学习者的发音，基于学习者口语响应的真实可懂度，这些口语响应是从支持向量机（SVM）分类器或深度学习神经网络模型对转录正确性的预测确定的。使用PocketSphinx对齐模式产生的数字特征和许多识别通道，搜索每个预期音素的替换和删除以及按顺序插入意外音素，SVM模型与Amazon Mechanical Turk crowdworker transmittance的准确率达到82%，高于多个独立研究人员报告的75%。使用这些功能与SVM分类器概率预测模型可以帮助计算机辅助发音教学（CAPT）系统提供可懂度补救。
英文摘要：
We use automatic speech recognition to assess spoken English learner pronunciation based on the authentic intelligibility of the learners' spoken responses determined from support vector machine (SVM) classifier or deep learning neural network model predictions of transcription correctness. Using numeric features produced by PocketSphinx alignment mode and many recognition passes searching for the substitution and deletion of each expected phoneme and insertion of unexpected phonemes in sequence, the SVM models achieve 82 percent agreement with the accuracy of Amazon Mechanical Turk crowdworker transcriptions, up from 75 percent reported by multiple independent researchers. Using such features with SVM classifier probability prediction models can help computer-aided pronunciation teaching (CAPT) systems provide intelligibility remediation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
