- The paper introduces Assem-VC, a modular non-parallel voice conversion system that combines state-of-the-art linguistic, intonation, and speaker encoding components.
- It details a methodology that optimally configures adversarial Cotatron, RAPT-based pitch estimation, and non-causal decoding enhanced with GTA finetuning for improved synthesis.
- Experiments demonstrate that Assem-VC achieves MOS and DMOS scores close to natural speech while effectively disentangling speaker identity from linguistic features.
Overview of "Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques"
The paper "Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques" tackles the challenge of enhancing non-parallel voice conversion systems by integrating advanced components of speech synthesis. The authors propose Assem-VC, an innovative voice conversion (VC) system that achieves state-of-the-art performance in converting the voice of a source speaker to that of a target speaker without altering the linguistic content. Their focus is on preserving not only the linguistic features but also the rhythm and intonation of speech, which are crucial for generating realistic and expressive audio outputs.
Approach and Methodology
The research decomposes prevailing VC systems into three essential components: a linguistic encoder, an intonation encoder, and a decoder. From these, the authors experiment and identify the optimal configuration, which includes:
- Linguistic Encoder: The paper evaluates PPG, Cotatron, and adversarial Cotatron linguistic encoders. Cotatron and its adversarial variant are noted for their ability to encapsulate linguistic features without leaking speaker identity through speaking rate.
- Intonation Encoder: The authors explore residual encoders and pitch contour estimation, favoring the latter (using RAPT) for its superior speaker independence.
- Decoder: A non-causal decoder is chosen over a causal architecture to better retain the speaker identity in the converted output, enhanced further by an additional speaker encoder utilizing conditional batch normalization.
The Assem-VC system is assembled from these components and evaluated for both many-to-many and any-to-many conversion scenarios. It leverages GTA (Ground-Truth Aligned) finetuning adapted from TTS systems to improve the quality of vocoder output, using HiFi-GAN as the vocoder.
Key Results
Assem-VC demonstrates significant advances over other systems like PPG-VC, Cotatron-VC, and Mellotron-VC:
- Performance Metrics: The proposed system achieves mean opinion scores (MOS) and degradation mean opinion scores (DMOS) that approximate those of natural speech, thus establishing new benchmarks for both many-to-many and any-to-many VC tasks.
- Speaker Disentanglement: Through speaker classification accuracy tests, Assem-VC verifies its capacity to disentangle speaker identity from the linguistic and intonation features, outperforming traditional methods.
- GTA Finetuning: The application of GTA finetuning is shown to significantly enhance speech naturalness and speaker similarity, indicating its previously untested potential in VC applications.
Implications and Future Directions
The implications of Assem-VC stretch across both theoretical and practical domains. The modular approach offers a blueprint for future VC systems, suggesting that individual component optimization can lead to substantial improvements in overall system performance. Practically, this system can be harnessed in entertainment sectors such as dubbing, voice personalization, and creating expressive audiobooks.
Further research could focus on refining adversarial training techniques to better address alignment issues noted in the paper and exploring the integration of additional modalities such as emotional context into the VC process. While the paper doesn't claim revolutionary breakthroughs, Assem-VC signifies a methodological leap in assembling and evaluating state-of-the-art components for voice conversion, setting the stage for future inquiry and application in artificial intelligence-driven speech synthesis.