SpeechSplit can perform aspect-specific voice conversion by disentangling
speech into content, rhythm, pitch, and timbre using multiple autoencoders in
an unsupervised manner. However, SpeechSplit requires careful tuning of the
autoencoder bottlenecks, which can be time-consuming and less robust. This
paper proposes SpeechSplit 2.0, which constrains the information flow of the
speech component to be disentangled on the autoencoder input using efficient
signal processing methods instead of bottleneck tuning. Evaluation results show
that SpeechSplit 2.0 achieves comparable performance to SpeechSplit in speech
disentanglement and superior robustness to the bottleneck size variations. Our
code is available at this https URL.

本文介绍了 SpeechSplit 2.0，它通过使用有效的信号处理方法来限制语音组件的信息流，从而实现了特定方面的语音转换和语音分离，相比于 SpeechSplit 在语音分离方面具有更高的稳健性和可靠性。

SpeechSplit 2.0: 无需调节自编码器瓶颈的无监督语音去混叠技术

SpeechSplit 2.0: Unsupervised speech disentanglement for voice conversion Without tuning autoencoder Bottlenecks

Speech information can be roughly decomposed into four components: language
content, timbre, pitch, and rhythm. Obtaining disentangled representations of
these components is useful in many speech analysis and generation applications.
Recently, state-of-the-art voice conversion systems have led to speech
representations that can disentangle speaker-dependent and independent
information. However, these systems can only disentangle timbre, while
information about pitch, rhythm and content is still mixed together. Further
disentangling the remaining speech components is an under-determined problem in
the absence of explicit annotations for each component, which are difficult and
expensive to obtain. In this paper, we propose SpeechSplit, which can blindly
decompose speech into its four components by introducing three carefully
designed information bottlenecks. SpeechSplit is among the first algorithms
that can separately perform style transfer on timbre, pitch and rhythm without
text labels. Our code is publicly available at
this https URL

本文介绍了 SpeechSplit，一个可以盲目将语音分解为其四个组成部分的算法，以及如何在没有文本标签的情况下分别在语调、音高和节奏上执行风格转换。