A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
The paper under review explores the domain of voice conversion, specifically investigating the roles of discrete and soft speech units in self-supervised representation learning. The core objective of voice conversion is to transform source speech into a target voice while maintaining the content unaffected. This paper provides an empirical investigation of discrete versus soft speech units and proposes methodologies to improve intelligibility and naturalness within this framework.
Methodological Insights
The paper articulates a comparison between discrete and soft speech units as input features for voice conversion systems. Discrete units, created by clustering audio features, effectively strip speaker information but at the cost of linguistic content loss, leading to mispronunciations. In contrast, soft speech units, introduced as a novel concept in this paper, model a distribution over discrete units, thereby capturing additional content information and enhancing the intelligibility and naturalness of speech conversion.
The researchers conducted experiments using two prominent self-supervised methods: Contrastive Predictive Coding (CPC) and Hidden-unit BERT (HuBERT). They formulated systems for any-to-one voice conversion and applied these systems to English intra-lingual and cross-lingual settings involving French and Afrikaans.
Experimental Results
The empirical results highlight several key findings:
- Intelligibility: Soft speech units showed significant improvements in both phoneme error rate (PER) and word error rate (WER) across tasks, demonstrating their efficacy in retaining linguistic content compared to discrete units.
- Speaker Similarity: Discrete speech units achieved near-perfect scores, confirming their ability to effectively remove speaker-specific details. However, soft units also performed well, although with a slight reduction in similarity due to the retention of more accent-related features in cross-lingual tasks.
- Naturalness: Mean opinion scores (MOS) for naturalness indicated a marked improvement when using soft units, suggesting enhanced prosody and fluency.
- Cross-lingual Transfer: Soft units extended their advantage over discrete units to unseen languages, showcasing better performance in transferring linguistic information across language boundaries.
Implications and Future Directions
The paper's findings have practical implications, notably in enhancing the performance of voice conversion systems in diverse applications such as entertainment and healthcare. By leveraging soft speech units, systems can achieve a better balance between content preservation and speaker variability suppression, leading to more natural-sounding converted speech.
The research also opens up avenues for further exploration, particularly in the field of any-to-any voice conversion and more complex linguistic constructions. Future investigations might focus on fine-tuning the balance between speaker similarity and intelligibility or exploring deeper integrations of these models with other natural language processing frameworks.
In summary, this paper provides a comprehensive analysis of discrete and soft speech units, proposing a robust methodological framework that advances the field of voice conversion. The integration of soft unit predictions presents a promising direction for enhancing both the intelligibility and naturalness of synthesized speech, which will undoubtedly influence future advancements in the domain.