arXiv:2512.20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech. We propose GenTSE, a two-stage decoder-only generative LM for TSE: Stage-1 predicts coarse semantic tokens, and Stage-2 generates fine acoustic tokens. Separating semantics and acoustics stabilizes decoding and yields more accurate target speech. Both stages use continuous SSL or codec embeddings, offering richer context than discretized-prompt methods. To reduce exposure bi
The continuous advancements in generative AI and language models are pushing the boundaries of speech processing, making sophisticated target speaker extraction techniques feasible.
Improved target speaker extraction has direct implications for more accurate voice assistants, enhanced surveillance, and more natural human-computer interaction, impacting various industries.
This innovation provides a more stable and accurate method for isolating specific voices from complex audio environments, potentially leading to more robust audio understanding systems.
- · AI-powered voice assistants
- · Surveillance technology providers
- · Telecommunications
- · Content creation platforms
- · Legacy speech separation techniques
- · Systems relying on noisy audio inputs
Higher fidelity speech interfaces will become more common, improving user experience and accessibility.
The ability to accurately extract individual voices could accelerate progress in personalized audio experiences and real-time dubbing.
Ethical and privacy concerns around voice tracking and synthesis may intensify, necessitating new regulatory frameworks.
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Read at arXiv cs.LG