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HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing., , , , and . ICASSP, page 2817-2821. IEEE, (2019)Grapheme and multilingual posterior features for under-resourced speech recognition: A study on Scottish Gaelic., , and . ICASSP, page 7334-7338. IEEE, (2013)Towards Directly Modeling Raw Speech Signal for Speaker Verification Using CNNS., , and . ICASSP, page 4884-4888. IEEE, (2018)Acoustic data-driven grapheme-to-phoneme conversion in the probabilistic lexical modeling framework., , and . Speech Commun., (2016)A Savitzky-Golay Filtering Perspective of Dynamic Feature Computation., , and . IEEE Signal Process. Lett., 20 (3): 281-284 (2013)Detection Of S1 And S2 Locations In Phonocardiogram Signals Using Zero Frequency Filter., , , and . ICASSP, page 1254-1258. IEEE, (2020)Articulatory feature based continuous speech recognition using probabilistic lexical modeling., and . Comput. Speech Lang., (2016)Applying Multi- and Cross-Lingual Stochastic Phone Space Transformations to Non-Native Speech Recognition., , , , and . IEEE Trans. Speech Audio Process., 21 (8): 1713-1726 (2013)HMM-Based Non-Native Accent Assessment Using Posterior Features., , and . INTERSPEECH, page 3137-3141. ISCA, (2016)Objective intelligibility assessment of text-to-speech systems through utterance verification., , , and . INTERSPEECH, page 3501-3505. ISCA, (2015)