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Polynomial Mixing for Efficient Self-supervised Speech Encoders

Eva Feillet, Ryan Whetten, David Picard, Alexandre Allauzen · Feb 28, 2026 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms. However, the quadratic complexity of self-attention in both memory and computation imposes significant constraints on scalability. In this work, we propose a novel token-mixing mechanism, the Polynomial Mixer (PoM), as a drop-in replacement for multi-head self-attention. PoM computes a polynomial representation of the input with linear complexity with respect to the input sequence length. We integrate PoM into a self-supervised speech representation learning framework based on BEST-RQ and evaluate its performance on downstream speech recognition tasks. Experimental results demonstrate that PoM achieves a competitive word error rate compared to full self-attention and other linear-complexity alternatives, offering an improved trade-off between performance and efficiency in time and memory.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms."

Evaluation Modes

provisional (inferred)

None explicit

Validate eval design from full paper text.

"State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms."

Reported Metrics

provisional (inferred)

Not extracted

No metric anchors detected.

"State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: No explicit eval keywords detected.
  • Potential metric signals: No metric keywords detected.
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • State-of-the-art speech-to-text models typically employ Transformer-based encoders that model token dependencies via self-attention mechanisms.
  • However, the quadratic complexity of self-attention in both memory and computation imposes significant constraints on scalability.
  • In this work, we propose a novel token-mixing mechanism, the Polynomial Mixer (PoM), as a drop-in replacement for multi-head self-attention.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

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