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Optimizing Vocabulary Modeling for Dysarthric Speech Recognition.

, and . ICCHP (2), volume 9759 of Lecture Notes in Computer Science, page 507-510. Springer, (2016)

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One-Pass Semi-Dynamic Network Decoding Using a Subnetwork Caching Model for Large Vocabulary Continuous Speech Recongnition., and . IEICE Trans. Inf. Syst., 87-D (5): 1164-1174 (2004)Pronunciation modeling of loanwords for Korean ASR using phonological knowledge and syllable-based segmentation., , and . APSIPA, page 430-435. IEEE, (2015)Korean Children's Spoken English Corpus and an Analysis of its Pronunciation Variability., , and . LREC, page 2362-2365. European Language Resources Association (ELRA), (2012)Modeling cross-morpheme pronunciation variations for korean large vocabulary continuous speech recognition., and . INTERSPEECH, page 261-264. ISCA, (2003)Mispronunciation Diagnosis of L2 English at Articulatory Level Using Articulatory Goodness-Of-Pronunciation Features., and . SLaTE, page 65-70. ISCA, (2017)Optimizing Vocabulary Modeling for Dysarthric Speech Recognition., and . ICCHP (2), volume 9759 of Lecture Notes in Computer Science, page 507-510. Springer, (2016)A Corpus-Based Study of English Pronunciation Variations., , and . INTERSPEECH, page 1893-1896. ISCA, (2011)Parallel Memory-Based Parsing on SNAP., and . IPPS, page 680-684. IEEE Computer Society, (1993)USC: description of the SNAP system used for MUC-5., , , , , , , and . MUC, page 305-319. ACL, (1993)Multilingual Analysis Of Intelligibility Classification Using English, Korean, And Tamil Dysarthric Speech Datasets., , and . O-COCOSDA 2022, page 1-6. IEEE, (2022)