Expressive Neural Voice Cloning
By Paarth Neekhara, Shehzeen Samarah Hussain, Shlomo Dubnov, Farinaz Koushanfar, Julian McAuley. 2021
40 citations recorded by the source corpus.
Voice cloning is the task of learning to synthesize the voice of an unseen speaker from a few samples. While current voice cloning methods achieve promising results in Text-to-Speech (TTS) synthesis for a new voice, these approaches lack the ability to control the expressiveness of synthesized audio. In this work, we propose a controllable voice cloning method that allows fine-grained control over various style aspects of the synthesized speech for an unseen speaker. We achieve this by explicitly conditioning the speech synthesis model on a speaker encoding, pitch contour and latent style tokens during training. Through both quantitative and qualitative evaluations, we show that our framework can be used for various expressive voice cloning tasks using only a few transcribed or untranscribed speech samples for a new speaker. These cloning tasks include style transfer from a reference speech, synthesizing speech directly from text, and fine-grained style control by manipulating the style conditioning variables during inference.
Subfields: Artificial Intelligence, Signal Processing
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Citations in the corpus
- A Survey on Neural Speech Synthesis (2021, methodology): “In this subsection, we review techniques on disentangling [218, 117, 275], controlling [353, 180, 240, 13, 267, 343, 192], and transferring [150, 131, 393, 6] variation information, as shown in Table 15.”
- Distribution Augmentation for Low-Resource Expressive Text-To-Speech (2022, unknown): “Many approaches were recently proposed to improve modelling of expressive speech [15, 16, 17, 18].”