  In this work, we propose a new mathematical vocoder algorithm(modified
spectral inversion) that generates a waveform from acoustic features without
phase estimation. The main benefit of using our proposed method is that it
excludes the training stage of the neural vocoder from the end-to-end speech
synthesis model. Our implementation can synthesize high fidelity speech at
approximately 20 Mhz on CPU and 59.6MHz on GPU. This is 909 and 2,702 times
faster compared to real-time. Since the proposed methodology is not a
data-driven method, it is applicable to unseen voices and multiple languages
without any additional work. The proposed method is expected to adapt for
researching on neural network models capable of synthesizing speech at the
studio recording level.
