PDF(Pubmed)
Abstract：
UNASSIGNED: This study explored the use of an automated language analysis tool, FLUCALC, for measuring fluency in aphasia. The purpose was to determine whether CLAN\'s FLUCALC command could produce efficient, objective outcome measures for salient aspects of fluency in aphasia.
UNASSIGNED: The FLUCALC command was used on CHAT transcripts of Cinderella stories from people with aphasia (PWA; n = 281) and controls (n = 257) in the AphasiaBank database.
UNASSIGNED: PWA produced significantly fewer total words, fewer words per minute, more pausing, more repetitions, more revisions, and more phonological fragments than controls, with only one exception: The Wernicke\'s group was similar to the control group in percentage of filled pauses. Individuals with Broca\'s aphasia had significantly longer inter-utterance pauses and fewer total words than all other aphasia groups. Both the Broca\'s and conduction aphasia groups had higher percentages of phrase repetitions than the NABW (NotAphasicByWAB) group. The conduction aphasia group also had a higher percentage of phrase revisions than the NABW and the anomic aphasia groups. Principal components analysis revealed two principal components that accounted for around 60% of the variance and related to quantity of output, rate of speech, and quality of output. The Gaussian mixture models showed that the participants clustered in three groups, which corresponded predominantly to the controls, the nonfluent aphasia group, and the remaining aphasia groups (all classically fluent aphasia types).
UNASSIGNED: FLUCALC is an efficient way to measure objective fluency behaviors in language samples in aphasia. Automated analyses of objective fluency behaviors on large samples of adults with and without aphasia can produce measures that can be used by researchers and clinicians to better understand and track salient aspects of fluency in aphasia.
UNASSIGNED: https://doi.org/10.23641/asha.25979863.
UNASSIGNED: The FLUCALC command was used on CHAT transcripts of Cinderella stories from people with aphasia (PWA; n = 281) and controls (n = 257) in the AphasiaBank database.
UNASSIGNED: PWA produced significantly fewer total words, fewer words per minute, more pausing, more repetitions, more revisions, and more phonological fragments than controls, with only one exception: The Wernicke\'s group was similar to the control group in percentage of filled pauses. Individuals with Broca\'s aphasia had significantly longer inter-utterance pauses and fewer total words than all other aphasia groups. Both the Broca\'s and conduction aphasia groups had higher percentages of phrase repetitions than the NABW (NotAphasicByWAB) group. The conduction aphasia group also had a higher percentage of phrase revisions than the NABW and the anomic aphasia groups. Principal components analysis revealed two principal components that accounted for around 60% of the variance and related to quantity of output, rate of speech, and quality of output. The Gaussian mixture models showed that the participants clustered in three groups, which corresponded predominantly to the controls, the nonfluent aphasia group, and the remaining aphasia groups (all classically fluent aphasia types).
UNASSIGNED: FLUCALC is an efficient way to measure objective fluency behaviors in language samples in aphasia. Automated analyses of objective fluency behaviors on large samples of adults with and without aphasia can produce measures that can be used by researchers and clinicians to better understand and track salient aspects of fluency in aphasia.
UNASSIGNED: https://doi.org/10.23641/asha.25979863.
摘要：
■这项研究探索了自动语言分析工具的使用，FLUCALC,用于测量失语症的流畅性。目的是确定CLAN的FLUCALC命令是否可以产生有效的，失语症流畅性显著方面的客观结果测量。
■FLUCALC命令用于失语症患者（PWA；n=281）和AphasiaBank数据库中的对照（n=257）的灰姑娘故事的CHAT转录。
■PWA产生的总单词明显减少，每分钟更少的单词，更多的暂停，更多的重复，更多修订,和更多的语音片段比控制，只有一个例外：Wernicke组的停顿填充百分比与对照组相似。与所有其他失语症组相比，Broca失语症患者的话语间停顿时间明显更长，总单词数量更少。Broca和传导性失语症组的短语重复百分比均高于NABW（NotAphasicByWAB）组。传导失语症组的短语修订百分比也高于NABW和失语症组。主成分分析揭示了两个主成分，它们占方差的60％左右，并且与产出数量有关，语速,和输出质量。高斯混合模型显示，参与者聚集在三组中，主要对应于控件，非流利的失语症组，和其余的失语症组（所有经典流利的失语症类型）。
■FLUCALC是一种有效的方法，可以在失语症中测量语言样本中的客观流利行为。对有失语症和没有失语症的成年人的大量样本的客观流畅性行为的自动分析可以产生可以被研究人员和临床医生用来更好地理解和跟踪失语症的流畅性的突出方面的措施。
■https://doi.org/10.23641/asha.25979863.
■FLUCALC命令用于失语症患者（PWA；n=281）和AphasiaBank数据库中的对照（n=257）的灰姑娘故事的CHAT转录。
■PWA产生的总单词明显减少，每分钟更少的单词，更多的暂停，更多的重复，更多修订,和更多的语音片段比控制，只有一个例外：Wernicke组的停顿填充百分比与对照组相似。与所有其他失语症组相比，Broca失语症患者的话语间停顿时间明显更长，总单词数量更少。Broca和传导性失语症组的短语重复百分比均高于NABW（NotAphasicByWAB）组。传导失语症组的短语修订百分比也高于NABW和失语症组。主成分分析揭示了两个主成分，它们占方差的60％左右，并且与产出数量有关，语速,和输出质量。高斯混合模型显示，参与者聚集在三组中，主要对应于控件，非流利的失语症组，和其余的失语症组（所有经典流利的失语症类型）。
■FLUCALC是一种有效的方法，可以在失语症中测量语言样本中的客观流利行为。对有失语症和没有失语症的成年人的大量样本的客观流畅性行为的自动分析可以产生可以被研究人员和临床医生用来更好地理解和跟踪失语症的流畅性的突出方面的措施。
■https://doi.org/10.23641/asha.25979863.
