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To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models ...
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RNN Classification of English Vowels: Nasalized or Not
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Abstract:
Vowel nasality is perceived and used by English listeners though it is not phonemic. Feature-based classifiers have been built to evaluate what features are useful for nasality perception and measurement. These classifiers require heavy high-level feature engineering with most features discrete and measured at discrete points. Recurrent neural networks can take advantage of sequential information, and has the advantage of freeing us from high-level feature engineering and potentially being stronger simulation models with a holistic view. Therefore, we constructed two types of RNN classifiers (vanilla RNN and LSTM) with MFCCs of the vowel as input to predict whether the vowel is nasalized or not. The LSTM model achieved the best performance, and supports the phonetic claim about the degree of coarticulatory nasality and the use of MFCCs for automatic speech recognition.
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Keyword:
classification; Computational Linguistics; perception; Phonetics and Phonology; recurrent neural network; vowel nasalization
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URL: https://scholarworks.umass.edu/scil/vol2/iss1/36 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1084&context=scil
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The focal alteration and causal connectivity in children with new-onset benign epilepsy with centrotemporal spikes
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Studies on the Differences Between Chinese and Western Nature Poems
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In: Studies in Literature and Language; Vol 10, No 3 (2015): Studies in Literature and Language; 83-88 ; 1923-1563 ; 1923-1555 (2015)
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Prediction of age, sentiment, and connectivity from social media text
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