Polynomial Mixing for Efficient Self-supervised Speech Encoders
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 t...
| Authors | Eva Feillet, Ryan Whetten, David Picard et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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