Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control

• URL: http://arxiv.org/abs/2111.09146v1
• Date: Wed, 17 Nov 2021 14:31:55 GMT
• Title: Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control
• Authors: Konstantinos Markopoulos, Nikolaos Ellinas, Alexandra Vioni, Myrsini Christidou, Panos Kakoulidis, Georgios Vamvoukakis, Georgia Maniati, June Sig Sung, Hyoungmin Park, Pirros Tsiakoulis and Aimilios Chalamandaris
• Abstract summary: A text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice. It utilizes a Tacotron-based multispeaker acoustic model trained on read-only speech data. Results show that the proposed approach can produce high quality rapping/singing voice with increased naturalness.
• Score: 47.33830090185952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice. It utilizes a Tacotron-based multispeaker acoustic model trained on read-only speech data and which provides prosody control at the phoneme level. Dataset augmentation and additional prosody manipulation based on traditional DSP algorithms are also investigated. The neural TTS model is fine-tuned to an unseen speaker's limited recordings, allowing rapping/singing synthesis with the target's speaker voice. The detailed pipeline of the system is described, which includes the extraction of the target pitch and duration values from an a capella song and their conversion into target speaker's valid range of notes before synthesis. An additional stage of prosodic manipulation of the output via WSOLA is also investigated for better matching the target duration values. The synthesized utterances can be mixed with an instrumental accompaniment track to produce a complete song. The proposed system is evaluated via subjective listening tests as well as in comparison to an available alternate system which also aims to produce synthetic singing voice from read-only training data. Results show that the proposed approach can produce high quality rapping/singing voice with increased naturalness.

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