Borderless Long Speech Synthesis
Abstract
Most existing text-to-speech (TTS) systems either synthesize speech sentence by sentence and stitch the results together, or drive synthesis from plain-text dialogues alone. Both approaches leave models with little understanding of global context or paralinguistic cues, making it hard to capture real-world phenomena such as multi-speaker interactions (interruptions, overlapping speech), evolving emotional arcs, and varied acoustic environments. We introduce the Borderless Long Speech Synthesis framework for agent-centric, borderless long audio synthesis. Rather than targeting a single narrow task, the system is designed as a unified capability set spanning VoiceDesigner, multi-speaker synthesis, Instruct TTS, and long-form text synthesis. On the data side, we propose a "Labeling over filtering/cleaning" strategy and design a top-down, multi-level annotation schema we call Global-Sentence-Token. On the model side, we adopt a backbone with a continuous tokenizer and add Chain-of-Thought (CoT) reasoning together with Dimension Dropout, both of which markedly improve instruction following under complex conditions. We further show that the system is Native Agentic by design: the hierarchical annotation doubles as a Structured Semantic Interface between the LLM Agent and the synthesis engine, creating a layered control protocol stack that spans from scene semantics down to phonetic detail. Text thereby becomes an information-complete, wide-band control channel, enabling a front-end LLM to convert inputs of any modality into structured generation commands, extending the paradigm from Text2Speech to borderless long speech synthesis.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the Borderless Long Speech Synthesis framework, which innovatively integrates multi-dimensional annotations and contextual understanding into TTS systems, significantly advancing the state-of-the-art in audio synthesis. The technical contributions and proposed methodologies offer substantial improvements over existing systems, although further experimental validation and reproducibility efforts are necessary to solidify its impact in the field.
Methodology
The proposed methodology introduces a novel framework for long-form speech synthesis that emphasizes the importance of global context and paralinguistic cues. The "Labeling over filtering/cleaning" strategy is innovative, as it challenges conventional practices in data preparation by advocating for the inclusion of complex, noisy data that reflects real-world speech dynamics. The Global-Sentence-Token hierarchical annotation schema is a significant advancement, enabling a structured approach to capturing the nuances of speech synthesis. The integration of Chain-of-Thought reasoning and Dimension Dropout enhances the model's ability to follow complex instructions, which is a notable methodological improvement over existing TTS systems.
Results & evaluation
The paper lacks quantitative evaluations of the proposed system's performance, particularly in terms of emotional arc coherence and multi-speaker interaction naturalness. While it discusses the challenges of evaluating borderless long audio synthesis, it does not provide concrete experimental results or comparisons with existing methods. The absence of benchmark results limits the ability to assess the system's effectiveness rigorously. Future work is needed to establish robust evaluation metrics that can capture the richness of the proposed framework.
Reproducibility
The paper does not provide sufficient implementation details or access to code and datasets, which raises concerns about reproducibility. The lack of a demo or project URL further complicates the ability for other researchers to replicate the findings or build upon this work. Clearer documentation and shared resources would enhance reproducibility.
Limitations
The system is currently optimized for content creation rather than real-time interactions, which limits its applicability in dynamic environments. Additionally, the training data is primarily speech-centric, and the system's emergent capabilities for sound effects and music are not fully developed. These limitations suggest that while the framework is promising, it requires further refinement and expansion to address broader applications.
Broader impact
The potential applications of this research extend beyond traditional TTS systems, offering possibilities for enhanced audio experiences in content creation, gaming, and virtual environments. The ability to synthesize speech with rich emotional and contextual cues could significantly improve user engagement and interaction quality in various multimedia applications. However, the challenges in real-time synthesis and the need for more diverse training data must be addressed to realize its full impact. The main contribution of this paper is the introduction of the Borderless Long Speech Synthesis framework, which innovatively integrates multi-dimensional annotations and contextual understanding into TTS systems, significantly advancing the state-of-the-art in audio synthesis. The technical contributions and proposed methodologies offer substantial improvements over existing systems, although further experimental validation and reproducibility efforts are necessary to solidify its impact in the field.
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