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Automatic Accent Assessment Using Phonetic Mismatch and Human Perception

机译:使用语音不匹配和人类感知能力进行自动口音评估

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In this study, a new algorithm for automatic accent evaluation of native and non-native speakers is presented. The proposed system consists of two main steps: alignment and scoring. In the alignment step, the speech utterance is processed using a Weighted Finite State Transducer (WFST) based technique to automatically estimate the pronunciation mismatches (substitutions, deletions, and insertions). Subsequently, in the scoring step, two scoring systems which utilize the pronunciation mismatches from the alignment phase are proposed: (i) a WFST-scoring system to measure the degree of accentedness on a scale from ${-}1$ (non-native like) to ${+}1$ (native like), and a (ii) Maximum Entropy (ME) based technique to assign perceptually motivated scores to pronunciation mismatches. The accent scores provided from the WFST-scoring system as well as the ME scoring system are termed as the WFST and P-WFST (perceptual WFST) accent scores, respectively. The proposed systems are evaluated on American English (AE) spoken by native and non-native (native speakers of Mandarin-Chinese) speakers from the CU-Accent corpus. A listener evaluation of 50 Native American English (N-AE) was employed to assist in validating the performance of the proposed accent assessment systems. The proposed P-WFST algorithm shows higher and more consistent correlation with human evaluated accent scores, when compared to the Goodness Of Pronunciation (GOP) measure. The proposed solution for accent classification and assessment based on WFST and P-WFST scores show that an effective advancement is possible which correlates well with human perception.
机译:在这项研究中,提出了一种自动评估母语和非母语使用者口音的新算法。拟议的系统包括两个主要步骤:对齐和评分。在对齐步骤中,使用基于加权有限状态换能器(WFST)的技术处理语音发声,以自动估计发音不匹配(替换,删除和插入)。随后,在评分步骤中,提出了两个利用对齐阶段的发音不匹配的评分系统:(i)WFST评分系统以 $ {-} 1 $ (非本地变量)转换为 $ {+} 1 $ < / tex> (自然之类),以及(ii)基于最大熵(ME)的技术来将感知动机得分分配给发音不匹配。由WFST评分系统和ME评分系统提供的重音分数分别称为WFST和P-WFST(感知WFST)重音分数。拟议的系统是根据CU-Accent语料库的母语和非母语(说普通话的母语)的美国英语(AE)进行评估的。使用50名美国原住民英语(N-AE)的听众评估来帮助验证所提出的口音评估系统的性能。与语音质量(GOP)度量相比,拟议的P-WFST算法与人类评估的口音分数显示出越来越高的一致性。所提出的基于WFST和P-WFST分数的口音分类和评估解决方案表明,有效的进步是有可能的,这与人类的感知能力密切相关。

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