This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach, which utilizes text supervision during training. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. During the training stage, an encoder-decoder-based hybrid connectionist-temporal-classification-attention (CTC-attention) phoneme recognizer is trained, whose encoder has a bottle-neck layer. A BNE is obtained from the phoneme recognizer and is utilized to extract speaker-independent, dense and rich spoken content representations from spectral features. Then a multi-speaker location-relative attention based seq2seq synthesis model is trained to reconstruct spectral features from the bottle-neck features, conditioning on speaker representations for speaker identity control in the generated speech. To mitigate the difficulties of using seq2seq models to align long sequences, we down-sample the input spectral feature along the temporal dimension and equip the synthesis model with a discretized mixture of logistic (MoL) attention mechanism. Since the phoneme recognizer is trained with large speech recognition data corpus, the proposed approach can conduct any-to-many voice conversion. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity. Ablation studies are conducted to confirm the effectiveness of feature selection and model design strategies in the proposed approach. The proposed VC approach can readily be extended to support any-to-any VC (also known as one/few-shot VC), and achieve high performance according to objective and subjective evaluations.

翻译：本文建议采用任何到多个位置(seq2seq)的排序到顺序(seq2seq),即非平行语音转换方法,该方法在培训期间使用文本监督。在这个方法中,我们将一个瓶装内颈特征提取器(BNE)与一个后部2seq合成模块结合起来。在培训阶段,一个基于编码器-脱coder的混合连接点-时装分级识别器(CT-attention)的语音识别器(CT-attention)进行了培训,其编码有瓶装层。从电话识别器中获取了一个不单位识别器,并且从音效转换器中提取了一个不单位识别器,并使用类似音介面识别器的输入光谱模型,同时用于从光速转换功能中提取一个不依赖、密度和丰富的口述内容。