With recent advancements in voice cloning, the performance of speech
synthesis for a target speaker has been rendered similar to the human level.
However, autoregressive voice cloning systems still suffer from text alignment
failures, resulting in an inability to synthesize long sentences. In this work,
we propose a variant of attention-based text-to-speech system that can
reproduce a target voice from a few seconds of reference speech and generalize
to very long utterances as well. The proposed system is based on three
independently trained components: a speaker encoder, synthesizer and universal
vocoder. Generalization to long utterances is realized using an energy-based
attention mechanism known as Dynamic Convolution Attention, in combination with
a set of modifications proposed for the synthesizer based on Tacotron 2.
Moreover, effective zero-shot speaker adaptation is achieved by conditioning
both the synthesizer and vocoder on a speaker encoder that has been pretrained
on a large corpus of diverse data. We compare several implementations of voice
cloning systems in terms of speech naturalness, speaker similarity, alignment
consistency and ability to synthesize long utterances, and conclude that the
proposed model can produce intelligible synthetic speech for extremely long
utterances, while preserving a high extent of naturalness and similarity for
short texts.

本文介绍了一种基于注意力机制和零样本说话人自适应技术，在语音克隆技术中可以从几秒钟的参考语音中复制目标语音，从而实现长话语的普遍化，并且可以保持较高的自然度和相似性。