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The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap.

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Acoustic Modeling for Distant Multi-talker Speech Recognition with Single- and Multi-channel Branches., , , , , and . ICASSP, page 6630-6634. IEEE, (2019)End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification., , , , and . CoRR, (2020)Online End-to-End Neural Diarization with Speaker-Tracing Buffer., , , , and . CoRR, (2020)Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition., , , , and . SemEval@COLING, page 1126-1134. International Committee for Computational Linguistics, (2020)End-To-End Speaker Diarization as Post-Processing., , , , and . ICASSP, page 7188-7192. IEEE, (2021)Synthetic Data Augmentation for ASR with Domain Filtering., , , , and . APSIPA ASC, page 1760-1765. IEEE, (2023)Semi-Supervised Training with Pseudo-Labeling for End-To-End Neural Diarization., , , , , and . Interspeech, page 3096-3100. ISCA, (2021)Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models., , , , and . INTERSPEECH, page 2218-2222. ISCA, (2022)Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR., , , , , , and . INTERSPEECH, page 1248-1252. ISCA, (2019)Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization., , , and . SLT, page 620-625. IEEE, (2022)