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MASK+: Data-driven regions selection for acoustic fingerprinting., , and . ICASSP, page 335-339. IEEE, (2015)The Zero Resource Speech Challenge 2019: TTS Without T., , , , , , , , , and 3 other author(s). INTERSPEECH, page 1088-1092. ISCA, (2019)Deriving Spectro-temporal Properties of Hearing from Speech Data., , , and . ICASSP, page 411-415. IEEE, (2019)Bayesian joint-sequence models for grapheme-to-phoneme conversion., , , , and . ICASSP, page 2836-2840. IEEE, (2017)Discovering Acoustic Units from Speech: A Bayesian Approach. (Découverte d'unités acoustiques dans la parole : une approche Bayésienne).. Brno University of Technology, Czech Republic, (2021)base-search.net (ftunivtbrno:oai:https://dspace.vut.cz:11012/196805).A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery., , , , and . CoRR, (2020)Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings., , , and . ICASSP, page 3747-3751. IEEE, (2019)GPU-Accelerated Forward-Backward Algorithm with Application to Lattice-Free MMI., , , , and . ICASSP, page 8417-8421. IEEE, (2022)Automatic Speech Recognition and Topic Identification from Speech for Almost-Zero-Resource Languages., , , , , , , , and . INTERSPEECH, page 2052-2056. ISCA, (2018)Variational Inference for Acoustic Unit Discovery., , and . SLTU, volume 81 of Procedia Computer Science, page 80-86. Elsevier, (2016)