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Speech Synthesis along Perceptual Voice Quality Dimensions
Frederik Rautenberg, Michael Kuhlmann, Fritz Seebauer +3
While expressive speech synthesis or voice conversion systems mainly focus on controlling or manipulating abstract prosodic characteristics of speech, such as emotion or accent, we…
Speaker and Style Disentanglement of Speech Based on Contrastive Predictive Coding Supported Factorized Variational Autoencoder
Yuying Xie, Michael Kuhlmann, Frederik Rautenberg +2
Speech signals encompass various information across multiple levels including content, speaker, and style. Disentanglement of these information, although challenging, is important…
Investigation into Target Speaking Rate Adaptation for Voice Conversion
Michael Kuhlmann, Fritz Seebauer, Janek Ebbers +2
Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a…
Contrastive Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised Learning of Disentangled Speech Representations
Janek Ebbers, Michael Kuhlmann, Tobias Cord-Landwehr +1
In this work we address disentanglement of style and content in speech signals. We propose a fully convolutional variational autoencoder employing two encoders: a content encoder a…