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Automatic Allocation of Training Data for Speech Understanding Based on Multiple Model Combinations., , , , , and . IEICE Trans. Inf. Syst., 95-D (9): 2298-2307 (2012)Improving Efficiency and Scalability of Model-Based Music Recommender System Based on Incremental Training., , , , and . ISMIR, page 89-94. Austrian Computer Society, (2007)Instrument Equalizer for Query-by-Example Retrieval: Improving Sound Source Separation Based on Integrated Harmonic and Inharmonic Models., , , , and . ISMIR, page 133-138. (2008)Domain-independent spoken dialogue platform using key-phrase spotting based on combined language model., , , and . INTERSPEECH, page 1319-1322. ISCA, (2001)Unsupervised Segmentation of Phoneme Sequences based on Pitman-Yor Semi-Markov Model using Phoneme Length Context., and . IJCNLP(1), page 243-252. Asian Federation of Natural Language Processing, (2017)Generating effective confirmation and guidance using two-level confidence measures for dialogue systems., and . INTERSPEECH, page 648. ISCA, (2000)Transformer-Based Physiological Feature Learning for Multimodal Analysis of Self-Reported Sentiment., , and . ICMI, page 349-358. ACM, (2022)Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words., and . COLING, page 161-170. ACL, (2016)Binarized Knowledge Graph Embeddings., , , , and . ECIR (1), volume 11437 of Lecture Notes in Computer Science, page 181-196. Springer, (2019)Automatic Allocation of Training Data for Rapid Prototyping of Speech Understanding based on Multiple Model Combination., , , , , and . COLING (Posters), page 579-587. Chinese Information Processing Society of China, (2010)