Skip to content
aiqbench takeAI Research

aiqbench analysis

This study investigates how non-speech interference causes representation drift in frozen speech encoders, revealing that embedding shifts do not always correlate directly with task performance loss across recognition and verification tasks. The research highlights critical nuances in robustness where interference during pauses induces significant drift even when it does not degrade intent or speaker verification capabilities as severely as interference during active speech. These findings are essential for developing resilient audio models capable of handling real-world acoustic environments without compromising core functionality.
Sources coveringarXiv, AI Alignment Forum
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Quieter Than the Room: Representation Drift and Task Robustness in Speech Encoders

arXiv2 min read

arXiv:2609.27195v1 Announce Type: cross Abstract: Non-speech interference can change a speech representation without causing comparable task loss. We test eight frozen encoders on four tasks, adding non-speech sounds throughout recordings, during speech, or in pauses. Under whole-recording interference, embedding drift tracks task loss across seven sounds, with mean Spearman correlations of 0.81-0.88. Moving the same sound between speech and pauses changes this pattern. At quiet to moderate levels, pause interference produces larger drift, while speech interference usually causes greater loss on intent recognition, speaker verification and speech recognition. Emotion recognition shows a weaker placement effect. Pause interference also changes speech-frame representations beyond the injected region. Even below the estimated recording background, interference can change embeddings as much as repeated speech takes do. Drift helps rank the effects of different sounds, but larger drift does not consistently indicate greater task loss.

Abstract:Non-speech interference can change a speech representation without causing comparable task loss. We test eight frozen encoders on four tasks, adding non-speech sounds throughout recordings, during speech, or in pauses. Under whole-recording interference, embedding drift tracks task loss across seven sounds, with mean Spearman correlations of 0.81-0.88. Moving the same sound between speech and pauses changes this pattern. At quiet to moderate levels, pause interference produces larger drift, while speech interference usually causes greater loss on intent recognition, speaker verification and speech recognition. Emotion recognition shows a weaker placement effect. Pause interference also changes speech-frame representations beyond the injected region. Even below the estimated recording background, interference can change embeddings as much as repeated speech takes do. Drift helps rank the effects of different sounds, but larger drift does not consistently indicate greater task loss.

Sound (cs.SD); Computation and Language (cs.CL)

arXiv:2609.27195 [cs.SD]

(or arXiv:2609.27195v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.27195 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 232 words

Related coverage