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- W2073450104 abstract "This paper proposes an automatic pronunciation scoring framework using learning to rank and class-normalized, dynamic-programming-based quantization. The goal is to train a model that is able to grade the pronunciation of a second language learner, such that the predicted score is as close as possible to the one given by a human teacher. Under this framework, each utterance is given a score of 1 to 5 by human raters, which is treated as a ground truth rank for the training algorithm. The corpus was rated by qualified English teachers in Taiwan (nonnative speakers). Nine phone-level scores are computed and converted into word-level scores through four conversion methods. We select the 16 best performing scores as the input features to train the learning-to-rank function. The output of the function is then quantized to a discrete rank on a 1-5 scale. The quantization is done with class normalization to alleviate the problem of data imbalance over different classes. Experimental results show that the proposed framework achieves a higher correlation to the human scores than other methods, along with higher accuracy in detecting instances of mispronunciation. We also release a new version of our nonnative corpus with human rankings." @default.
- W2073450104 created "2016-06-24" @default.
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- W2073450104 date "2015-11-01" @default.
- W2073450104 modified "2023-09-27" @default.
- W2073450104 title "Automatic Pronunciation Scoring with Score Combination by Learning to Rank and Class-Normalized DP-Based Quantization" @default.
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- W2073450104 doi "https://doi.org/10.1109/taslp.2015.2449089" @default.
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