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Towards Automatic Assessment of Self-Supervised Speech Models using Rank

Zakaria Aldeneh, Vimal Thilak, Takuya Higuchi, Barry-John Theobald, Tatiana Likhomanenko

TL;DR

The paper addresses the challenge of evaluating self-supervised speech encoders without labeled data by extending RankMe to the temporal domain as $RankMe-t$, enabling unsupervised monitoring of representation quality in speech SSL models. Using HuBERT Base trained on LibriSpeech with multiple SSL configurations, the authors show that $RankMe-t$ correlates with downstream performance within individual layers across in-domain and out-of-domain tasks, though it does not reliably identify the single best layer for a given task. The findings support the use of RankMe-t as a resource-efficient tool for tracking training progress and selecting checkpoints, while acknowledging that layer choice for downstream tasks remains task-dependent. Overall, the work provides a practical unsupervised metric that links embedding rank to downstream potential in speech SSL and motivates further investigation into layer-wise representation learning.

Abstract

This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessing the performance of these encoders is resource-intensive and requires labeled data from the downstream tasks. Inspired by the vision domain, where embedding rank has shown promise for evaluating image encoders without tuning on labeled downstream data, this work examines its applicability in the speech domain, considering the temporal nature of the signals. The findings indicate rank correlates with downstream performance within encoder layers across various downstream tasks and for in- and out-of-domain scenarios. However, rank does not reliably predict the best-performing layer for specific downstream tasks, as lower-ranked layers can outperform higher-ranked ones. Despite this limitation, the results suggest that embedding rank can be a valuable tool for monitoring training progress in SSL speech models, offering a less resource-demanding alternative to traditional evaluation methods.

Towards Automatic Assessment of Self-Supervised Speech Models using Rank

TL;DR

The paper addresses the challenge of evaluating self-supervised speech encoders without labeled data by extending RankMe to the temporal domain as , enabling unsupervised monitoring of representation quality in speech SSL models. Using HuBERT Base trained on LibriSpeech with multiple SSL configurations, the authors show that correlates with downstream performance within individual layers across in-domain and out-of-domain tasks, though it does not reliably identify the single best layer for a given task. The findings support the use of RankMe-t as a resource-efficient tool for tracking training progress and selecting checkpoints, while acknowledging that layer choice for downstream tasks remains task-dependent. Overall, the work provides a practical unsupervised metric that links embedding rank to downstream potential in speech SSL and motivates further investigation into layer-wise representation learning.

Abstract

This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessing the performance of these encoders is resource-intensive and requires labeled data from the downstream tasks. Inspired by the vision domain, where embedding rank has shown promise for evaluating image encoders without tuning on labeled downstream data, this work examines its applicability in the speech domain, considering the temporal nature of the signals. The findings indicate rank correlates with downstream performance within encoder layers across various downstream tasks and for in- and out-of-domain scenarios. However, rank does not reliably predict the best-performing layer for specific downstream tasks, as lower-ranked layers can outperform higher-ranked ones. Despite this limitation, the results suggest that embedding rank can be a valuable tool for monitoring training progress in SSL speech models, offering a less resource-demanding alternative to traditional evaluation methods.
Paper Structure (9 sections, 3 equations, 2 figures, 2 tables)

This paper contains 9 sections, 3 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background: RankMe
  3. RankMe-:: RankMe Temporal Extension
  4. Setup
  5. SSL Model Training
  6. Downstream Model Training
  7. Rank Measurement
  8. Results
  9. Discussion & Conclusion

Figures (2)

  • Figure 1: The rank ($y$-axis), estimated via RankMe-$t$, dependence on the training step ($x$-axis) for a given layer when training HuBERT. Different points for a given layer and step represent different hyper-parameter settings described in Section \ref{['sec:setup:ssl_model_training']}.
  • Figure 2: The figure shows a positive correlation between the downstream performance (y-axis) and the embedding rank (x-axis) estimated via RankMe-$t$ for each layer ($0$th, $3$rd, $6$th, $9$th) on the four tasks described in Table \ref{['table_tasks']}. Different points for the same layer correspond to different hyper-parameters settings for SSL training. Despite the positive correlations that we observe per layer, we cannot use rank to predict which layer performs the best on a downstream task.