AdaptVC: High Quality Voice Conversion with Adaptive Learning (2501.01347v4)

Abstract: The goal of voice conversion is to transform the speech of a source speaker to sound like that of a reference speaker while preserving the original content. A key challenge is to extract disentangled linguistic content from the source and voice style from the reference. While existing approaches leverage various methods to isolate the two, a generalization still requires further attention, especially for robustness in zero-shot scenarios. In this paper, we achieve successful disentanglement of content and speaker features by tuning self-supervised speech features with adapters. The adapters are trained to dynamically encode nuanced features from rich self-supervised features, and the decoder fuses them to produce speech that accurately resembles the reference with minimal loss of content. Moreover, we leverage a conditional flow matching decoder with cross-attention speaker conditioning to further boost the synthesis quality and efficiency. Subjective and objective evaluations in a zero-shot scenario demonstrate that the proposed method outperforms existing models in speech quality and similarity to the reference speech.

• The paper presents AdaptVC, a novel voice conversion method using adapters with self-supervised learning models to disentangle content and speaker identity for high-quality zero-shot conversion.
• Experimental evaluation shows AdaptVC surpasses existing models like kNN-VC and DiffVC in zero-shot scenarios, achieving lower WER/CER and higher MOS scores for quality and similarity.
• AdaptVC simplifies parameter tuning and enhances efficiency, demonstrating significant potential for real-time voice conversion and broader applications in speech technology.

Overview of "AdaptVC: High Quality Voice Conversion with Adaptive Learning"

The paper on AdaptVC presents a novel approach to the field of voice conversion (VC), focusing on the challenge of converting a source speaker's voice to resemble a target speaker's voice while maintaining the original content. Voice conversion technologies have wide-ranging practical applications such as in personalized text-to-speech systems, privacy protection, and language learning tools. The authors address the critical issue of disentangling linguistic content from speaker identity, particularly in zero-shot scenarios where the source and target speakers are not seen during training.

Methodology

AdaptVC builds upon the self-supervised learning (SSL) framework, utilizing the diversified features from SSL models to achieve high-quality voice conversion. The authors propose a key innovation: the use of adapters to combine information from various intermediate layers of a self-supervised speech model (such as HuBERT). This approach allows AdaptVC to achieve a nuanced capture of speech characteristics.

1. Adaptive Encoding:

• Content Encoder: Utilizes adapters tuned with vector quantization to ensure the encoding primarily captures the linguistic content, discouraging entanglement with speaker characteristics.
• Speaker Encoder: Processes speech to extract frame-wise speaker-specific features independent of the underlying linguistic content for precise vocal attribute matching.

2. Conditional Flow Matching Decoder:

The AdaptVC architecture employs a Conditional Flow Matching (CFM) decoder. This is aimed at improving both speech quality and synthesis efficiency. The decoder is enhanced with cross-attention to provide robust speaker conditioning, thereby ensuring that the voice conversion closely aligns with the target speaker profile.

Experimental Evaluation

The authors provide substantial empirical evidence through subjective and objective evaluations in zero-shot scenarios, demonstrating that AdaptVC surpasses existing models like kNN-VC, DiffVC, and DDDM-VC in terms of speech intelligibility and fidelity to target speaker characteristics. The results from experiments include:

• Word Error Rate (WER) and Character Error Rate (CER): AdaptVC demonstrates lower error rates compared to its counterparts, signifying its superior model robustness.
• Mean Opinion Score (MOS): Higher MOS ratings for both naturalness (MOS-N) and speaker similarity (MOS-S) confirm the perceived quality of the converted speech.
• Speaker Embedding Cosine Similarity (SECS): A high SECS value suggests an effective retention of target speaker characteristics.

Implications and Future Directions

AdaptVC represents a significant development in voice conversion, as the model design simplifies the parameter tuning process typical in self-supervised learning by automatically adapting layer outputs. This has substantial implications for real-time voice conversion applications, offering speed and efficiency.

Future Developments:

• Scalability and Portability: Further work could explore AdaptVC's scalability across diverse datasets, languages, and accents.
• Fine-Grained Control: Enhancing control over individual voice attributes, such as pitch and emotional tone, could broaden its applications.

AdaptVC pushes forward both theoretical and practical boundaries in speech synthesis and voice conversion. As techniques for disentangling complex auditory features advance, systems like AdaptVC will likely become integral components in various voice-driven technologies.