Karaoker: Alignment-free singing voice synthesis with speech training data

• URL: http://arxiv.org/abs/2204.04127v1
• Date: Fri, 8 Apr 2022 15:33:59 GMT
• Title: Karaoker: Alignment-free singing voice synthesis with speech training data
• Authors: Panos Kakoulidis, Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos, June Sig Sung, Gunu Jho, Pirros Tsiakoulis, Aimilios Chalamandaris
• Abstract summary: Karaoker is a multispeaker Tacotron-based model conditioned on voice characteristic features. The model is jointly conditioned with a single deep convolutional encoder on continuous data. We extend the text-to-speech training objective with feature reconstruction, classification and speaker identification tasks.
• Score: 3.9795908407245055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a multispeaker Tacotron-based model conditioned on voice characteristic features that is trained exclusively on spoken data without requiring time-alignments. Karaoker synthesizes singing voice following a multi-dimensional template extracted from a source waveform of an unseen speaker/singer. The model is jointly conditioned with a single deep convolutional encoder on continuous data including pitch, intensity, harmonicity, formants, cepstral peak prominence and octaves. We extend the text-to-speech training objective with feature reconstruction, classification and speaker identification tasks that guide the model to an accurate result. Except for multi-tasking, we also employ a Wasserstein GAN training scheme as well as new losses on the acoustic model's output to further refine the quality of the model.

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