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Latent Words Recurrent Neural Network Language Models for Automatic Speech Recognition., , , , and . IEICE Trans. Inf. Syst., 102-D (12): 2557-2567 (2019)Domain Adaptation Based on Mixture of Latent Words Language Models for Automatic Speech Recognition., , , , , and . IEICE Trans. Inf. Syst., 101-D (6): 1581-1590 (2018)Audio Visual Scene-Aware Dialog Generation with Transformer-based Video Representations., , , , and . CoRR, (2022)Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss., , , , , , , , , and 1 other author(s). CoRR, (2023)Neural candidate-aware language models for speech recognition., , and . Comput. Speech Lang., (2021)Large-Context Pointer-Generator Networks for Spoken-to-Written Style Conversion., , and . ICASSP, page 8189-8193. IEEE, (2020)Domain Adversarial Self-Supervised Speech Representation Learning for Improving Unknown Domain Downstream Tasks., , , , , , and . INTERSPEECH, page 1066-1070. ISCA, (2022)Neural Error Corrective Language Models for Automatic Speech Recognition., , , and . INTERSPEECH, page 401-405. ISCA, (2018)Combinations of various language model technologies including data expansion and adaptation in spontaneous speech recognition., , , , , and . INTERSPEECH, page 463-467. ISCA, (2015)Training data selection for acoustic modeling via submodular optimization of joint kullback-leibler divergence., , , , and . INTERSPEECH, page 3645-3649. ISCA, (2015)