UniSAE: Unified Speech Attribute Editing on Speaker, Emotion and Low-Level Content via Discrete Phonetic Posteriorgram Modelling
arXiv:2606.31128v1 Announce Type: cross Abstract: Speech editing aims to modify specific portions of an utterance while preserving the remaining speech. Existing approaches primarily focus on word-level content modification and typically treat content, speaker, and emotion editing as separate tasks, limiting both editing granularity and flexibility. We propose UniSAE, a unified speech attribute editing framework which supports composable speaker, emotion and content editing from sub-phoneme to word level within a single architecture. UniSAE introduces a Discrete Phonetic PosteriorGram (DPPG) r
The continuous advancements in AI, especially in speech synthesis and editing, are pushing towards more granular and multi-attribute control, leading to unified frameworks like UniSAE. This is happening as AI models become more sophisticated in dissecting and manipulating complex data types.
This development allows for highly flexible and detailed manipulation of spoken content, speaker identity, and emotional tone within a single AI framework, which has significant implications for media production, accessibility tools, and synthetic media creation. A strategic reader should care because it represents a leap in AI's ability to generate and modify human-like communication with greater control and efficiency.
Speech editing shifts from siloed, task-specific approaches to a unified, multi-attribute system, enabling composable, sub-phoneme to word-level modifications. This fundamentally changes how synthetic voices and modulated speech content can be generated and customized.
- · Media production companies
- · Synthetic media developers
- · Accessibility technology providers
- · AI speech models developers
- · Traditional voice acting industry (niche areas)
- · Companies with less sophisticated speech editing tools
- · Forensic voice analysis (potentially, due to increased 합성 realism)
More realistic and customizable AI-generated speech and voiceovers become available for various applications.
Increased efficiency in content localization, character voice development for gaming/film, and personalized communication through speech interfaces.
Potential blurring of lines between real and synthetic speech, requiring new methods for content authentication and ethical guidelines for AI voice manipulation.
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Read at arXiv cs.CL