Phone-Level Prosody Modelling with GMM-Based MDN for Diverse and Controllable Speech Synthesis
This work addresses the challenge of generating diverse and controllable prosody in text-to-speech systems, representing an incremental improvement over prior methods.
The paper tackled the problem of limited diversity in speech synthesis prosody by proposing a GMM-based mixture density network for phone-level prosody modeling, achieving significantly better diversity and naturalness compared to single Gaussian methods, with prosody cloning showing comparable similarity to fine-grained VAE and better speaker similarity.
Generating natural speech with a diverse and smooth prosody pattern is a challenging task. Although random sampling with phone-level prosody distribution has been investigated to generate different prosody patterns, the diversity of the generated speech is still very limited and far from what can be achieved by humans. This is largely due to the use of uni-modal distribution, such as single Gaussian, in the prior works of phone-level prosody modelling. In this work, we propose a novel approach that models phone-level prosodies with a GMM-based mixture density network(MDN) and then extend it for multi-speaker TTS using speaker adaptation transforms of Gaussian means and variances. Furthermore, we show that we can clone the prosodies from a reference speech by sampling prosodies from the Gaussian components that produce the reference prosodies. Our experiments on LJSpeech and LibriTTS dataset show that the proposed method with GMM-based MDN not only achieves significantly better diversity than using a single Gaussian in both single-speaker and multi-speaker TTS, but also provides better naturalness. The prosody cloning experiments demonstrate that the prosody similarity of the proposed method with GMM-based MDN is comparable to recent proposed fine-grained VAE while the target speaker similarity is better.