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Abstract Neural vocoders can synthesize high-quality speech waveforms from acoustic features, but they cannot control by acoustic parameters, such as $F_0$ and formant frequencies. Although analysis-synthesis based on signal processing can be controlled using acoustic parameters, its speech quality is inferior to that of neural vocoders. This paper proposes End-to-End Neural Formant Synthesis for generating high-quality speech waveforms with controllable acoustic parameters from low-dimensional representations. We compared three models with different structures, and investigated their synthesis quality and controllability.