Abstract:

For the purpose of relieving the time cost and inconformity in annotation, the authors use an articulatory features based mispronunciation detection system to give an Top-N feedback and use this feedback to assist manual annotation. As a result, the consistency rate of phoneme labels in proposed system increases from 80.7% to 92.48%. In addition, the time cost for annotating each sentence reduce from 10 to 3 minutes. The results indicate that proposed automatic annotation system is practical, and there is also a room for further improvement.

Key words: articulatory features (AFs), pronunciation erroneous tendency (PET), automatic annotation

摘要：

针对发音偏误检测系统语音标注费时、费力和标注不一致的问题, 基于发音特征, 构建偏误检测系统, 给出Top-N的识别结果, 通过praat软件呈现机器初步标注文本, 在此基础上进行人工二次标注。实验结果表明, 与单纯的人工标注相比, 所提出的自动标注加人工二次标注方法在标注一致性上从80.7%提高到92.48%, 平均每个句子的标注时间从10分钟减少到3分钟。所提方法有效地提高了人工标注的效率, 可以在有限时间内为识别系统提供更多可靠的标注语料。

关键词: 发音特征, 发音偏误趋势, 自动标注