Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score
arXiv:2607.01527v1 Announce Type: cross Abstract: Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when speaker, room, and source-receiver geometry remain unchanged, degrading downstream task performance. We propose a framework that learns room embeddings robust to speech-content variation and a representation-level uncertainty score from reverberant speech without downstream-task supervision. The embedding is anchored to a structured room impulse response (RIR) latent space and trained using a mult
The proliferation of AI models interacting with real-world sensory data, particularly speech, necessitates robust methods for handling data uncertainty and degradation in diverse environments.
This development addresses a critical limitation in AI systems relying on auditory input, improving reliability and performance in real-world applications where speech and environmental variables are often unreliable.
AI systems can now better quantify and mitigate the uncertainty in 'room embeddings' derived from reverberant speech, leading to more stable and trustworthy auditory scene analysis and potentially better human-AI interaction.
- · AI developers
- · Speech recognition companies
- · Smart home device manufacturers
- · Robotics
- · Systems with high reliance on uncalibrated audio input
Improved performance and accuracy of AI systems in complex acoustic environments due to better handling of data uncertainty.
Accelerated development of robust AI agents and interactive systems that depend on reliable auditory perception.
Enhanced user trust and adoption of AI-powered devices operating in diverse, real-world soundscapes.
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Read at arXiv cs.LG