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However, the\ndifficulties optimizing the PDD criteria without auxiliary losses result in\nquality degradation of synthesized speech. To generate more natural speech\nsignals within the teacher-student framework, we propose a novel optimization\ncriterion based on generative adversarial networks (GANs). In the proposed\nmethod, the inverse autoregressive flow-based student model is incorporated as\na generator in the GAN framework, and jointly optimized by the PDD mechanism\nwith the proposed adversarial learning method. As this process encourages the\nstudent to model the distribution of realistic speech waveform, the perceptual\nquality of the synthesized speech becomes much more natural. Our experimental\nresults verify that the PWG systems with the proposed method outperform both\nthose using conventional approaches, and also autoregressive generation systems\nwith a well-trained teacher WaveNet.","url_abs":"http://arxiv.org/abs/1904.04472v1","url_pdf":"http://arxiv.org/pdf/1904.04472v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"190404472","repo_url":"https://github.com/markliou/model_distillation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.04472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04472"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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