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Polynomial Mixing for Efficient Self-supervised Speech Encoders

arXiv:2603.00683v1 Announce Type: new 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.

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Eva Feillet, Ryan Whetten, David Picard, Alexandre Allauzen · · 1 min read · 2 views
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arXiv:2603.00683v1 Announce Type: new 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.

Executive Summary

This article proposes a novel polynomial mixing mechanism, the Polynomial Mixer (PoM), as a drop-in replacement for multi-head self-attention in Transformer-based speech-to-text models. PoM computes a polynomial representation of the input with linear complexity, addressing the quadratic complexity of self-attention. The authors integrate PoM into a self-supervised speech representation learning framework and evaluate its performance on downstream speech recognition tasks. The results demonstrate competitive word error rates compared to full self-attention and other linear-complexity alternatives, offering a promising trade-off between performance and efficiency. The proposed solution tackles a significant challenge in the field, making it a valuable contribution to the advancement of speech recognition technology.

Key Points

  • ▸ The authors propose the Polynomial Mixer (PoM), a novel token-mixing mechanism with linear complexity.
  • ▸ PoM is integrated into a self-supervised speech representation learning framework and evaluated on speech recognition tasks.
  • ▸ The results show competitive performance with full self-attention and other linear-complexity alternatives.

Merits

Scalability improvement

PoM addresses the quadratic complexity of self-attention, enabling more efficient processing of longer input sequences.

Competitive performance

The experimental results demonstrate that PoM achieves competitive word error rates compared to full self-attention and other linear-complexity alternatives.

Demerits

Limited evaluation

The article primarily focuses on speech recognition tasks, and its applicability to other areas, such as machine translation or text summarization, is not explored.

Potential computational overhead

The polynomial representation computed by PoM may introduce additional computational overhead, which could offset the benefits of linear complexity.

Expert Commentary

The proposed Polynomial Mixer (PoM) mechanism is a significant contribution to the field of speech recognition, addressing a critical challenge in scalability. While the experimental results are promising, further investigation is needed to fully understand the potential computational overhead and limitations of PoM. Additionally, exploring its applicability to other areas, such as machine translation or text summarization, could provide valuable insights into its broader potential. As the field continues to evolve, it is likely that PoM will play a prominent role in the development of more efficient and effective speech recognition systems.

Recommendations

  • ✓ Future research should focus on exploring the applicability of PoM to other areas of natural language processing and investigating its potential computational overhead.
  • ✓ The development of more efficient speech recognition technology has significant implications for policy makers, and further research should consider the broader societal impact of such advancements.

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