Objective

To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers.

Methods

Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain “gold standard” transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions.

Results

For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction.

Conclusion

Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.

中文翻译：

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使用机器学习对自动转录进行后处理，以进行语言流畅度评分

客观的

将手动转录得出的言语流利度得分与使用机器学习分类器增强的自动语音识别获得的言语流利度得分进行比较。

方法

使用 Amazon Web Services，我们自动转录了 1400 名执行动物和字母 F 言语流畅性任务的人的言语流畅性录音。我们手动调整自动转录的时间和内容，以获得“黄金标准”转录。为了实现自动评分，我们训练了机器学习分类器来区分有效和无效的话语。然后，我们计算并比较了手动和自动转录的言语流利度得分。

结果

对于动物和字母流利性任务，我们实现了有效话语与无效话语的良好分离。基于自动转录计算的言语流利度分数与手动校正后计算的语言流利度分数显示出高度相关性。

结论

许多对言语流利度单词列表进行评分的技术都需要准确的转录和单词计时。我们证明，机器学习方法可以用于为此目的改进现成的 ASR。这些自动得出的分数对于某些应用程序可能是令人满意的。一些分数之间的低相关性表明在可靠地实施全自动方法之前需要改进自动语音识别。