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Exploring Transfer Learning for Low Resource Emotional TTS.

, , and . IntelliSys (1), volume 1037 of Advances in Intelligent Systems and Computing, page 52-60. Springer, (2019)

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Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs., , , and . CoRR, (2024)Laughter Synthesis: Combining Seq2seq Modeling with Transfer Learning., , and . INTERSPEECH, page 3401-3405. ISCA, (2020)ICE-Talk 2: Interface for Controllable Expressive TTS with perceptual assessment tool., , and . Softw. Impacts, (2021)Introducing AmuS: The Amused Speech Database., , , , , , and . SLSP, volume 10583 of Lecture Notes in Computer Science, page 229-240. Springer, (2017)Towards a listening agent: a system generating audiovisual laughs and smiles to show interest., , , , and . ICMI, page 248-255. ACM, (2016)The Limitations of Current Similarity-Based Objective Metrics in the Context of Human-Agent Interaction Applications., , , , , , , and . ICMI Companion, page 81-85. ACM, (2023)Exploring Transfer Learning for Low Resource Emotional TTS., , and . IntelliSys (1), volume 1037 of Advances in Intelligent Systems and Computing, page 52-60. Springer, (2019)Emotional Speech Datasets for English Speech Synthesis Purpose: A Review., , and . IntelliSys (1), volume 1037 of Advances in Intelligent Systems and Computing, page 61-66. Springer, (2019)An HMM approach for synthesizing amused speech with a controllable intensity of smile., , , , and . ISSPIT, page 7-11. IEEE Computer Society, (2015)AVAB-DBS: an Audio-Visual Affect Bursts Database for Synthesis., , , and . LREC, European Language Resources Association (ELRA), (2016)