We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora. Many SC frameworks require parallel corpora, phonetic alignments, or explicit frame-wise correspondence for learning conversion functions or for synthesizing a target spectrum with the aid of alignments. However, these requirements gravely limit the scope of practical applications of SC due to scarcity or even unavailability of parallel corpora. We propose an SC framework based on variational auto-encoder which enables us to exploit non-parallel corpora. The framework comprises an encoder that learns speaker-independent phonetic representations and a decoder that learns to reconstruct the designated speaker. It removes the requirement of parallel corpora or phonetic alignments to train a spectral conversion system. We report objective and subjective evaluations to validate our proposed method and compare it to SC methods that have access to aligned corpora.

本文提出了基于可变自动编码器的光谱转换 SC 框架，以利用非平行语料库进行训练，消除与使用平行语料库或音素对齐有关的限制，包括编码器和解码器来学习光谱转换系统，通过客观和主观评价验证了该方法的有效性并与其他 SC 方法进行了比较。

使用变分自编码器从非步调一致的语料库进行语音转换