MultiVerse: A Zero-Shot Multi-Task TTS System for Efficient and Expressive Speech Synthesis with Limited Data

מושגי ליבה

MultiVerse is a novel text-to-speech (TTS) system that achieves high-quality, zero-shot, multi-task performance in various conditions (including cross-lingual and speech style transfer) with significantly less training data than traditional data-driven approaches, by leveraging source-filter theory-based disentanglement and a hybrid prosody modeling approach.

תקציר

• Bibliographic Information: Bak, T., Eom, Y., Choi, S., & Joo, Y.-S. (2024). MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech. arXiv preprint arXiv:2410.03192v1.
• Research Objective: This paper introduces MultiVerse, a novel zero-shot multi-task TTS system that aims to achieve high-quality speech synthesis and style transfer in various conditions (including zero-shot and cross-lingual settings) while requiring significantly less training data than traditional data-driven approaches.
• Methodology: MultiVerse leverages source-filter theory to decompose speech generation into filter-related and source-related representations, utilizing prompts for modeling both. It employs a prompt-based autoregressive prosody predictor to model acoustic features and a non-autoregressive source generator for frame-level prosody refinement. The model is trained using a combination of reconstruction loss, adversarial loss, and acoustic feature loss.
• Key Findings: Evaluations demonstrate that MultiVerse achieves comparable zero-shot TTS performance to data-driven TTS systems with significantly less data and outperforms other zero-shot TTS systems trained with the same amount of data. The proposed prosody modeling technique enables MultiVerse to generate speech with a high degree of prosody similarity to given prompts.
• Main Conclusions: MultiVerse effectively addresses the limitations of existing zero-shot TTS systems by achieving high-quality speech synthesis and style transfer in various conditions with limited training data. The source-filter-based disentanglement and hybrid prosody modeling contribute to its efficiency and expressiveness.
• Significance: This research significantly advances the field of zero-shot TTS by proposing a data-efficient and expressive model, potentially enabling wider accessibility and application of TTS technology, especially for low-resource languages.
• Limitations and Future Research: MultiVerse relies on a separate neural vocoder, whose performance can impact the overall quality. Future research could explore incorporating unsupervised acoustic feature modeling to further enhance efficiency and explore the potential of the model for low-resource languages.

סטטיסטיקה

MultiVerse achieves comparable zero-shot synthesis in both timbre and prosody with only 1/60 of the training data compared to VALL-E.
MultiVerse was trained on approximately 1.2k hours of English and Korean speech data.
VALL-E was trained on over 60k hours of English speech data.
VALL-EX was trained on over 70k hours of English and Chinese speech data.

ציטוטים

"To expand TTS applications in zero-shot conditions, it is crucial to ensure generalization across various speech components, such as content, style, and speaker identity."
"In this paper, we introduce a multi-task TTS system, called MultiVerse, enabling speech synthesis and speech style transfer in zero-shot and cross-lingual conditions, requiring significantly less data compared to the data-driven approaches and featuring enhanced prosody modeling."
"Evaluation results demonstrate that MultiVerse not only achieves zero-shot TTS performance comparable to data-driven TTS systems with much less data, but also significantly outperforms other zero-shot TTS systems trained with the same small amount of data."

תובנות מפתח מזוקקות מ:

MultiVerse: Efficient and Expressive Zero-Shot Multi-Task Text-to-Speech

by Taejun Bak, ... ב- arxiv.org 10-07-2024

https://arxiv.org/pdf/2410.03192.pdf

שאלות מעמיקות

How might the development of increasingly sophisticated TTS systems like MultiVerse impact the accessibility of information and communication for individuals with speech impairments or language barriers?

The development of increasingly sophisticated Text-to-Speech (TTS) systems like MultiVerse holds immense potential for revolutionizing accessibility for individuals with speech impairments or language barriers, empowering them with unprecedented levels of communication and information access. Here's how:

Breaking Communication Barriers: For individuals with speech impairments, TTS systems can act as a voice, converting their written text into spoken words. The high fidelity and naturalness of systems like MultiVerse, which excel in zero-shot multi-speaker TTS, can enable these individuals to communicate their thoughts and emotions more effectively, fostering greater social inclusion and participation.
Personalized Voices: The ability of MultiVerse to synthesize speech with a high degree of prosody similarity to given prompts opens doors for individuals to communicate with personalized voices that reflect their age, gender, and even emotional state. This can be particularly empowering for individuals who have lost their voices due to medical conditions, allowing them to maintain a sense of identity and continuity.
Bridging Language Gaps: The cross-lingual TTS capabilities of MultiVerse can be transformative for individuals facing language barriers. Imagine real-time translation of spoken words into different languages, enabling seamless communication across cultures and promoting global understanding. This can be particularly beneficial in educational, healthcare, and travel settings.
Access to Information: TTS systems can make digital content, such as online articles, e-books, and websites, accessible to individuals with visual impairments or learning disabilities who struggle with reading. The ability of MultiVerse to generate highly natural and expressive speech can make the experience more engaging and less fatiguing.
However, it's crucial to address potential challenges:

Cost and Availability: Ensuring that these advanced TTS systems are affordable and accessible to all, regardless of their socioeconomic background, is paramount.
Data Bias: Addressing potential biases in training data to ensure that synthesized voices are inclusive and representative of diverse accents, dialects, and speaking styles is critical.
Ethical Considerations: Establishing clear ethical guidelines for the use of TTS technology, particularly in sensitive contexts like voice cloning or impersonation, is essential.
By proactively addressing these challenges, we can harness the power of sophisticated TTS systems like MultiVerse to create a more inclusive and accessible world for everyone.

Could the reliance on source-filter disentanglement in MultiVerse potentially limit its ability to capture and synthesize more nuanced or complex vocal characteristics that are not easily separable into these two components?

While the source-filter disentanglement approach in MultiVerse offers advantages in achieving efficient and expressive zero-shot TTS with limited data, it's worth considering whether this reliance could potentially limit its ability to capture and synthesize more nuanced or complex vocal characteristics that might not be easily separable into these two components.
Here's a breakdown of the potential limitations:

Oversimplification of Vocal Production: The source-filter model, while a useful approximation, is a simplification of the complex process of human vocal production. There might be subtle vocal nuances, such as vocal fry, breathiness, or certain types of vocal tension, that are not fully captured by separating the vocal tract's resonance properties (filter) from the excitation signal produced by the vocal folds (source).
Interdependence of Source and Filter: In reality, the source and filter components of voice production are not entirely independent. The shape of the vocal tract can influence the vibration of the vocal folds, and vice versa. MultiVerse's approach, while incorporating some level of interaction between the representations, might not fully capture this complex interdependence.
Challenges with Non-Standard Vocalizations: TTS systems relying on source-filter disentanglement might face challenges synthesizing non-standard vocalizations like singing, whispering, or emotional expressions that significantly alter the typical source-filter characteristics.
However, MultiVerse incorporates mechanisms to mitigate some of these limitations:

Prompt-Based Modulation: The use of prompt speech in modeling both filter and source representations allows MultiVerse to capture some speaker-specific nuances that go beyond the basic source-filter model.
Two-Stage Prosody Modeling: The combination of autoregressive and non-autoregressive prosody modeling helps MultiVerse capture and synthesize a wider range of expressive variations in speech.
Future research directions:

Hybrid Approaches: Exploring hybrid models that combine the strengths of source-filter disentanglement with other techniques, such as those capturing vocal tract shaping or glottal flow dynamics, could lead to more nuanced voice synthesis.
Data Augmentation and Representation Learning: Training TTS systems on larger and more diverse datasets that include a wider range of vocal characteristics, along with advanced representation learning techniques, could help overcome the limitations of current disentanglement approaches.
By acknowledging these potential limitations and pursuing further research, we can strive to develop TTS systems that capture the full richness and complexity of human voice, including its most nuanced and expressive qualities.

If human language is inherently intertwined with cultural context and expression, how can TTS systems like MultiVerse be developed to authentically reflect and navigate these complexities in a way that is both accurate and culturally sensitive?

Developing TTS systems that authentically reflect the intricate tapestry of human language, interwoven with cultural context and expression, while upholding accuracy and cultural sensitivity, is a multifaceted challenge. Here's a roadmap for navigating these complexities:

Culturally Diverse Datasets: Training TTS systems on datasets that encompass a wide spectrum of languages, dialects, accents, and speaking styles is paramount. This requires moving beyond readily available data and actively seeking out and incorporating under-represented languages and cultural groups.
Contextual Embeddings: Incorporating contextual embeddings that capture the social, cultural, and emotional nuances of language use is crucial. This could involve training models on data annotated with cultural metadata or leveraging external knowledge bases that provide cultural context.
Prosodic Sensitivity: Prosody, encompassing intonation, rhythm, and stress, plays a vital role in conveying cultural nuances and emotional expression. TTS systems like MultiVerse, with its focus on prosody modeling, are well-positioned to address this. Training on data annotated with prosodic variations specific to different cultures can enhance authenticity.
Collaboration with Cultural Experts: Engaging with linguists, anthropologists, and cultural experts throughout the development process is essential. Their insights can help ensure that TTS systems accurately reflect cultural nuances and avoid perpetuating stereotypes or biases.
User Feedback and Iterative Design: Incorporating feedback from users belonging to diverse cultural backgrounds is crucial for identifying and rectifying any unintended biases or misrepresentations. Iterative design processes that prioritize user feedback can lead to more culturally sensitive and accurate TTS systems.
Ethical Guidelines and Transparency: Establishing clear ethical guidelines for the development and deployment of TTS systems is paramount. This includes being transparent about the limitations of the technology and the potential for misuse, particularly in culturally sensitive contexts.
By embracing these principles, we can strive to develop TTS systems that not only generate speech but also embody the rich tapestry of human language, reflecting its cultural nuances and fostering cross-cultural understanding and respect.