Analysis methods for assessing TTS intelligibility
Analysis methods for assessing TTS intelligibility
复制标题
评估 TTS 清晰度的分析方法
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Jason Lilley
中科院分区：
文献类型：
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作者：
H. Bunnell;Jason Lilley
语义不可预测（SU）句子经常被用来评估TTS系统的可懂度，但分析听者对SU句子的反应可能是一个劳动密集型的过程。在本文中，我们比较了几种方法，从SUS任务的数据分析。一项比较五种TTS系统的研究的数据进行了分析，从字符串编辑措施的基础上仔细的手纠正语音转录的反应，主要是未经纠正的单词或发音正确的措施。结果表明，一个简单的排序正确的措施是足够的，只有排名顺序信息的兴趣。然而，“清晰度校正”的测量掩盖了系统之间差异的大小，当测量系统之间可懂度的差异有多大很重要时，应该避免使用。在准备用于分析的响应数据时，对收听者响应数据的仔细的人类解释可以导致总体上更高的可懂度测量，但是不与TTS系统或其他因素交互，并且因此在比较多个TTS系统时不会导致不同的结论。这表明大部分自动评分程序是可行的。
Semantically unpredictable (SU) sentences are often used to assess intelligibility of TTS systems, but analyses of listener responses to SU sentences can be a labor-intensive process. In this paper we compare several approaches to the analysis of data from an SUS task. Data from a study comparing five TTS systems were analyzed in a variety of ways ranging from string edit measures based on carefully hand-corrected phonetically transcribed responses to largely uncorrected wordsor sentences-correct measures. Results suggest that a simple sentences-correct measure is adequate when only rank order information is of interest. However, the sentencescorrect measure masks the magnitude of differences between systems and should be avoided when it is important to gage how large the difference in intelligibility is between systems. In preparing response data for analysis, careful human interpretation of listener response data can lead to higher intelligibility measures overall, but does not interact with TTS system or other factors and consequently does not lead to different conclusions when comparing multiple TTS systems. This suggests that largely automated scoring procedures are feasible.