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Vocal fatigue induced by prolonged oral reading: Analysis and detection

机译：长时间阅读引起的声疲劳：分析和检测

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This article uses prolonged oral reading corpora for various experiments to analyze and detect vocal fatigue. Vocal fatigue particularly concerns voice professionals, including teachers, telemarketing operators, users of automatic speech recognition technology and actors. In analyzing and detecting vocal fatigue, we focused our investigations on three main experiments: a prosodic analysis that can be compared to the results found in related work, a two-class Support Vector Machines (SVM) classifier into Fatigue and Non-Fatigue states using a large set of audio features and a comparison function that estimates the difference in fatigue level between two speech segments using a combination of multiple phoneme-based comparison functions. The experiments on prosodic analysis showed that vocal fatigue was not associated with an increase in fundamental frequency and voice intensity. A two-class SVM classifier using the Paralinguistic Challenge 2010 audio feature set gave an unweighted accuracy of 94.1 % for the training set (10-fold cross-validation) and 68.2% for the test set. These results show that the phenomenon of vocal fatigue can be modeled and detected. The comparison function was assessed by detecting increased fatigue levels between two speech segments. The fatigue level detection performance in Equal Error Rate (EER) was 31 % using all phonetic segments and yielded EER of 21 % after filtering phonetic segments and 19% after filtering phonetic segments and cepstral features. These results show that some phonemes are more sensitive than others to vocal fatigue. These experiments show that the fatigued voice has specific characteristics for prolonged oral reading and suggest the feasibility of vocal fatigue detection.
机译：本文使用长时间的口头阅读语料库进行各种实验，以分析和检测声带疲劳。语音疲劳尤其涉及语音专业人员，包括教师，电话推销操作员，自动语音识别技术的用户和演员。在分析和检测人声疲劳时，我们将研究重点放在三个主要实验上：可以与相关工作中的结果进行比较的韵律分析，使用以下两种方法将疲劳状态和非疲劳状态分为两类：支持向量机（SVM）分类器大量的音频功能和比较功能，结合使用多个基于音素的比较功能，可以估算两个语音段之间的疲劳程度差异。韵律分析实验表明，声疲劳与基本频率和声音强度的增加无关。使用Paralinguistic Challenge 2010音频功能集的两类SVM分类器对训练集（10倍交叉验证）的未加权准确度为94.1％，对于测试集的未加权准确度为68.2％。这些结果表明，可以对声疲劳现象进行建模和检测。通过检测两个语音段之间疲劳程度的提高来评估比较功能。在所有语音段中，以平均错误率（EER）进行的疲劳水平检测性能为31％，过滤语音段后的EER为21％，过滤语音段和倒频谱特征后的EER为19％。这些结果表明，某些音素对语音疲劳比其他音素更敏感。这些实验表明，疲劳的声音具有长时间阅读的特定特征，并提示了语音疲劳检测的可行性。

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