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Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings.

, , , and . ICASSP, page 3747-3751. IEEE, (2019)

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Bayesian Subspace HMM for the Zerospeech 2020 Challenge., and . CoRR, (2020)The Zero Resource Speech Challenge 2019: TTS Without T., , , , , , , , , and 3 other author(s). INTERSPEECH, page 1088-1092. ISCA, (2019)MASK+: Data-driven regions selection for acoustic fingerprinting., , and . ICASSP, page 335-339. IEEE, (2015)Revisiting joint decoding based multi-talker speech recognition with DNN acoustic model., , , , , , , and . CoRR, (2021)Deriving Spectro-temporal Properties of Hearing from Speech Data., , , and . ICASSP, page 411-415. IEEE, (2019)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)Bayesian joint-sequence models for grapheme-to-phoneme conversion., , , , and . ICASSP, page 2836-2840. IEEE, (2017)Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings., , , and . ICASSP, page 3747-3751. IEEE, (2019)A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery., , , , and . CoRR, (2020)Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery., , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2022)