Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations (2110.14513v2)

Abstract: We present a neural analysis and synthesis (NANSY) framework that can manipulate voice, pitch, and speed of an arbitrary speech signal. Most of the previous works have focused on using information bottleneck to disentangle analysis features for controllable synthesis, which usually results in poor reconstruction quality. We address this issue by proposing a novel training strategy based on information perturbation. The idea is to perturb information in the original input signal (e.g., formant, pitch, and frequency response), thereby letting synthesis networks selectively take essential attributes to reconstruct the input signal. Because NANSY does not need any bottleneck structures, it enjoys both high reconstruction quality and controllability. Furthermore, NANSY does not require any labels associated with speech data such as text and speaker information, but rather uses a new set of analysis features, i.e., wav2vec feature and newly proposed pitch feature, Yingram, which allows for fully self-supervised training. Taking advantage of fully self-supervised training, NANSY can be easily extended to a multilingual setting by simply training it with a multilingual dataset. The experiments show that NANSY can achieve significant improvement in performance in several applications such as zero-shot voice conversion, pitch shift, and time-scale modification.

• The paper introduces the NANSY framework that reconstructs speech from self-supervised representations without relying on labeled data.
• It employs an innovative information perturbation strategy with advanced features like wav2vec and Yingram for enhanced speech synthesis.
• Experimental results demonstrate superior performance in zero-shot voice conversion, pitch shifting, and time-scale modification compared to bottleneck methods.

Analysis of the "Neural Analysis and Synthesis" Framework

The paper "Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations" by Choi et al. introduces the Neural Analysis and Synthesis (NANSY) framework, a significant advancement in the domain of speech processing. The framework utilizes a fully self-supervised approach, eschewing the need for labeled data such as transcriptions or speaker identities, to achieve high-quality, controlled manipulation of speech signals.

Framework Design

The NANSY framework is structured around a novel training strategy known as information perturbation. This method diverges from conventional approaches that rely on information bottlenecks, which often degrade reconstruction quality due to restricted information flow. Instead, NANSY introduces perturbations to input information, such as formant and pitch, allowing synthesis networks to selectively extract essential attributes, promoting both high-quality reconstruction and control over the output.

The framework leverages advanced speech representations, primarily wav2vec and a newly introduced pitch feature dubbed "Yingram." Wav2vec features are utilized for capturing linguistic information, exploiting the capabilities of highly multilingual pre-trained models. Yingram is posited as a superior alternative to fundamental frequency ($f_0$), particularly effective under challenging conditions such as sub-harmonics, enhancing pitch representation and manipulation.

Experimental Evaluation

The authors present comprehensive experiments demonstrating the efficacy of NANSY across various applications including zero-shot voice conversion, pitch shifting, and time-scale modification. In these tasks, NANSY consistently outperforms existing methods, particularly those that employ information bottleneck strategies, by achieving a favorable balance between speaker similarity and content preservation.

The framework's zero-shot voice conversion performance is noteworthy. It achieves high naturalness and speaker similarity scores across multiple language scenarios, indicating its robustness and applicability in diverse linguistic environments. This is augmented by the introduction of a Test-time Self-Adaptation (TSA) technique, which adjusts the input linguistic features at test time, thus significantly improving language adaptability without the need for fine-tuning model parameters.

Implications and Future Directions

NANSY's ability to operate in a fully self-supervised manner has substantial implications for tasks across multilingual and resource-constrained scenarios. It circumvents the limitations of text-dependent systems, offering a versatile solution for speech synthesis and conversion. The implications for multilingual speech processing are profound, providing a pathway for extending speech technology into underrepresented languages with minimal additional effort.

Looking forward, the integration of text information could further extend the framework's capabilities, facilitating more granular control in speech manipulation tasks. Additionally, advancements in detection algorithms for synthesized speech could address potential misuse in domains requiring authenticity verification.

In conclusion, the NANSY framework represents a significant advancement in speech synthesis, offering enhanced control and high-quality reconstruction without reliance on labeled datasets. This research sets a robust foundation for future exploration in self-supervised learning systems, promising further innovations across AI-driven speech technologies.