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On modeling context-dependent clustered states: Comparing HMM/GMM, hybrid HMM/ANN and KL-HMM approaches.

, , and . ICASSP, page 7659-7663. IEEE, (2014)

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Integrated pronunciation learning for automatic speech recognition using probabilistic lexical modeling., , and . ICASSP, page 5176-5180. IEEE, (2015)Improving Under-Resourced Language ASR Through Latent Subword Unit Space Discovery., and . INTERSPEECH, page 3873-3877. ISCA, (2016)An HMM-based formalism for automatic subword unit derivation and pronunciation generation., and . ICASSP, page 4639-4643. IEEE, (2015)On recognition of non-native speech using probabilistic lexical model., and . INTERSPEECH, page 26-30. ISCA, (2014)SMILE Swiss German Sign Language Dataset., , , , , , , , , and 2 other author(s). LREC, European Language Resources Association (ELRA), (2018)Acoustic data-driven grapheme-to-phoneme conversion in the probabilistic lexical modeling framework., , and . Speech Communication, (2016)HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing., , , , and . ICASSP, page 2817-2821. IEEE, (2019)On modeling context-dependent clustered states: Comparing HMM/GMM, hybrid HMM/ANN and KL-HMM approaches., , and . ICASSP, page 7659-7663. IEEE, (2014)Probabilistic lexical modeling and unsupervised training for zero-resourced ASR., , and . ASRU, page 446-451. IEEE, (2013)An Ensemble Model that Combines Syntactic and Semantic Clustering for Discriminative Dependency Parsing., , and . ACL (Short Papers), page 710-714. The Association for Computer Linguistics, (2011)