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Voice source waveforms for utterance level speaker identification using support vector machines.

, , and . CITA, page 1-7. IEEE, (2013)

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Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association., , , , , , , and . ALTA, Association for Computational Linguistics, (2023)Policy committee for adaptation in multi-domain spoken dialogue systems., , , , , and . ASRU, page 806-812. IEEE, (2015)On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems., , , , , , , and . ACL (1), The Association for Computer Linguistics, (2016)A Generative Model for Joint Natural Language Understanding and Generation., , , and . ACL, page 1795-1807. Association for Computational Linguistics, (2020)Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer., , , , , and . TL4NLP, volume 203 of Proceedings of Machine Learning Research, page 88-102. PMLR, (2022)Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems., , , , , and . EMNLP, page 1711-1721. The Association for Computational Linguistics, (2015)Counter-fitting Word Vectors to Linguistic Constraints., , , , , , , , and . HLT-NAACL, page 142-148. The Association for Computational Linguistics, (2016)Counter-fitting Word Vectors to Linguistic Constraints, , , , , , , , and . HLT-NAACL, (2016)Multi-domain Neural Network Language Generation for Spoken Dialogue Systems., , , , , , and . HLT-NAACL, page 120-129. The Association for Computational Linguistics, (2016)Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems., , , , , and . SIGDIAL Conference, page 417-421. The Association for Computer Linguistics, (2015)