Continuous Wavelet Vocoder-based Decomposition of Parametric Speech Waveform Synthesis

TL;DR

This paper discusses the use of continuous wavelet vocoders for decomposing parametric speech waveforms, aiming to improve speech synthesis quality and efficiency.

Contribution

It introduces a novel vocoder-based decomposition method utilizing continuous wavelet transforms for parametric speech waveform synthesis.

Findings

Enhanced speech quality with wavelet-based decomposition

Reduced computational complexity compared to neural network models

Potential for real-time speech synthesis applications

Abstract

To date, various speech technology systems have adopted the vocoder approach, a method for synthesizing speech waveform that shows a major role in the performance of statistical parametric speech synthesis. WaveNet one of the best models that nearly resembles the human voice, has to generate a waveform in a time consuming sequential manner with an extremely complex structure of its neural networks.