StarTSE: Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model
arXiv:2604.19635v2 Announce Type: replace-cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications. Direct adaptation to streaming scenarios often leads to catastrophic inference performance degradation due to the severe mismatch between training and streaming inference. To bridge this gap, we present the first autoregressive (AR) models tailored for streaming TSE. Our approach introduces a Chunk-wise Interleaved Splicing Paradigm that ensures highly efficient an
The continuous push for real-time AI applications necessitates novel architectural solutions for generative models that traditionally rely on global context.
Achieving streaming capabilities for advanced generative AI models like those used in Target Speaker Extraction unlocks new real-time applications and improves user experience across various sectors.
Streaming TSE models become viable for deployment, overcoming the performance degradation issues previously encountered when adapting generative models to real-time scenarios.
- · AI-powered communication platforms
- · Real-time voice processing companies
- · Developers of custom AI agents
- · Edge AI hardware manufacturers
- · Generative AI models limited to offline processing
- · Systems requiring high latency for audio source separation
Improved accuracy and responsiveness in real-time audio analysis and communication applications become widespread.
The proliferation of more sophisticated voice-controlled AI agents capable of operating effectively in complex, multi-speaker environments.
Enhanced accessibility features and personalized audio experiences become standard in consumer electronics and industrial interfaces.
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Read at arXiv cs.AI