Innovative Approach to Zero-Shot Multi-Speaker TTS with Negated Speaker Representations

Основные понятия

The author proposes a novel negation feature learning paradigm to improve zero-shot multi-speaker TTS by disentangling speaker attributes and reducing content leakage, leading to enhanced synthesis robustness and speaker fidelity.

Аннотация

The content discusses the challenges faced in multi-speaker TTS models and introduces a unique approach using negated speaker representations. By leveraging innovative techniques like multi-stream Transformers and adaptive layer normalizations, the proposed method aims to preserve speaker-specific attributes effectively. Extensive experiments validate the efficacy of this approach in achieving higher speaker similarity compared to baseline models.
Key points include:

Challenges in adapting multi-speaker TTS models to diverse speakers.
Proposal of a negation feature learning paradigm for improved synthesis robustness.
Utilization of multi-stream Transformers for capturing diverse speaker attributes.
Integration of adaptive layer normalizations for effective fusion of text and speaker representations.
Experimental results demonstrating superior performance over baseline models.

Статистика

"Our model surpasses the baseline systems across all three metrics for both seen and unseen speakers."
"MCD quantifies the difference between the kth Mel Frequency Cepstral Coefficient (MFCC) vectors of the synthesized ˆS and the ground truth audio signals S for frame T."
"Word Error Rate (WER) is used to evaluate the accuracy of word pronunciation in synthesized audio."

Цитаты

"Our negation scheme not only mitigates content leakage but also improves speaker fidelity."
"Through adaptive layer normalizations, target speaker attributes were fused into both the encoder and decoder of the baseline TTS model."

Ключевые выводы из

Enhancing Zero-Shot Multi-Speaker TTS with Negated Speaker Representations

by Yejin Jeon,Y... в arxiv.org 03-06-2024

https://arxiv.org/pdf/2401.02014.pdf

Дополнительные вопросы

How can this innovative approach impact other areas beyond text-to-speech synthesis

This innovative approach of utilizing negated speaker representations in multi-speaker TTS can have far-reaching impacts beyond text-to-speech synthesis. One significant application could be in the field of voice conversion, where the ability to accurately transform an individual's voice into that of another person is crucial. By disentangling speaker attributes from content information, this method could potentially enhance the accuracy and fidelity of voice conversion systems. Additionally, in areas like personalized virtual assistants or interactive AI applications, incorporating distinct voices for different speakers without extensive training data becomes more feasible with this approach. Moreover, industries such as entertainment and gaming could benefit from creating diverse character voices efficiently and realistically.

What counterarguments exist against utilizing negated speaker representations in enhancing multi-speaker TTS

While the use of negated speaker representations shows promise in enhancing multi-speaker TTS systems, there are potential counterarguments to consider. One argument against this approach could be related to overfitting or loss of contextual information during the negation process. By subtracting content features from audio representations, there might be a risk of losing subtle nuances or emotional cues present in the original speech signal that contribute to naturalness and expressiveness. Another counterargument could revolve around computational complexity and efficiency concerns when implementing complex arithmetic operations like subtraction on large-scale datasets or real-time applications.

How does disentanglement of target features relate to style transfer in different research domains

The concept of disentanglement of target features plays a vital role not only in multi-speaker TTS but also in various research domains such as style transfer across different modalities like images and text. In computer vision, disentangled feature learning has been instrumental in tasks like image style transfer where separating content from style enables generating artistic variations while preserving underlying structures effectively. Similarly, in machine translation research, disentangling translator-specific linguistic styles has led to producing translations tailored to individual translators' preferences accurately. This parallel between disentanglement for target features across domains underscores its significance in capturing specific attributes while maintaining flexibility and adaptability within generative processes.