arXiv:2607.08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable. We present PS4, a proxy-supervised training framework for TSE in real conversational mixtures, with two main contributions. First, we construct a large-scale corpus of 71,771 training samples derived from four public datasets, covering both Chinese and English scenarios. Each sample contains an overlapping speech mixture, per-speaker enrollment audio, a
Advances in AI research, particularly in areas requiring robust data for complex tasks like target speaker extraction, are continually pushing the boundaries of what is possible, as evidenced by the development of PS4.
Improving target speaker extraction in real conversational mixtures addresses a critical bottleneck for many AI applications, enabling more accurate and reliable voice-controlled systems and intelligent agents.
This development allows for more effective training of TSE models using real-world, noisy data, moving beyond the limitations of synthetic datasets and improving performance in practical scenarios.
- · AI developers
- · Voice assistant companies
- · Call center technology providers
- · Security and surveillance sectors
- · Companies reliant on less sophisticated audio processing
- · Early-stage AI solutions with poor noise resilience
More accurate and robust AI applications will emerge that can process human speech in complex, real-world environments.
This advancement could accelerate the development and adoption of AI agents, as their ability to understand and differentiate human speech improves significantly.
Enhanced target speaker extraction capabilities may lead to unforeseen privacy concerns or ethical questions as AI systems become more adept at individual voice identification within crowds.
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Read at arXiv cs.AI