ANCHOR: New Method for Assessing Speech Quality from Partial Audio in Real-Time Systems
Researchers have proposed ANCHOR, an autoregressive neural model that assesses speech quality incrementally from partial audio rather than requiring a complete utterance. It extends a prior system called ARECHO by introducing dual-resolution tokens and a hierarchical decoder that jointly models chunk-level and utterance-level quality. The work addresses a practical gap in streaming and generative speech systems, where full-context quality predictors currently degrade significantly on incomplete inputs.
ANCHOR (Autoregressive Non-intrusive Chunk-Ordered Refinement) is a speech quality estimation model accepted at Interspeech 2026 that reformulates incremental audio assessment as a multi-resolution autoregressive task. Unlike existing non-intrusive predictors that assume access to a complete utterance, ANCHOR operates on prefix-constrained inputs — a requirement of real-world streaming and generative audio pipelines. The model uses a single decoder with dual-resolution tokens to simultaneously produce coarse chunk-level and fine utterance-level quality scores in a hierarchical, coarse-to-fine manner. Experiments demonstrate a 48% reduction in PLCMOS error on 2-second audio prefixes compared to prior approaches. A convergence analysis identifies an effective perceptual context horizon of approximately 4 to 6 seconds, suggesting that beyond this window, additional audio contributes diminishing returns to quality estimation. A stress test further reveals structured extrapolation biases when localized corruption is introduced, pointing to areas for future robustness work. The authors conclude that hierarchical supervision both improves incremental prediction accuracy and sheds light on how perceptual quality accumulates over time.
What's missing
The paper does not detail the specific datasets or languages used for evaluation, nor does it compare ANCHOR against a broad range of baseline systems beyond ARECHO, leaving its generalizability to diverse acoustic conditions and languages unclear. The PLCMOS metric used is itself a model-based proxy for human perception, and no direct human listening evaluation is reported.
What different sources said
- arXiv cs.LGCenter
ANCHOR: Autoregressive Non-intrusive Chunk-Ordered Refinement for Joint Multi-Resolution Speech Quality Modeling
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