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Analysing the Masked predictive coding training criterion for pre-training a Speech Representation Model

Hemant Yadav, Sunayana Sitaram, Rajiv Ratn Shah

TL;DR

The paper investigates how masked predictive coding (MPC) during HuBERT pre-training shapes the type of information learned across speech-layer representations. It compares two MPL configurations with different layer placements and label counts, evaluating the resulting representations on nine SUPERB probing tasks via linearly weighted per-layer features. The main finding is that content information increases as MPC loss is minimized and is concentrated near the loss location, while speaker information tends to reside in earlier layers and cannot be directly controlled by MPC. These results inform the design of future pre-training criteria and tasks that aim to preserve both content and speaker information across layers, highlighting the limits of MPC alone for universal speech representation learning.

Abstract

Recent developments in pre-trained speech representation utilizing self-supervised learning (SSL) have yielded exceptional results on a variety of downstream tasks. One such technique, known as masked predictive coding (MPC), has been employed by some of the most high-performing models. In this study, we investigate the impact of MPC loss on the type of information learnt at various layers in the HuBERT model, using nine probing tasks. Our findings indicate that the amount of content information learned at various layers of the HuBERT model has a positive correlation to the MPC loss. Additionally, it is also observed that any speaker-related information learned at intermediate layers of the model, is an indirect consequence of the learning process, and therefore cannot be controlled using the MPC loss. These findings may serve as inspiration for further research in the speech community, specifically in the development of new pre-training tasks or the exploration of new pre-training criterion's that directly preserves both speaker and content information at various layers of a learnt model.

Analysing the Masked predictive coding training criterion for pre-training a Speech Representation Model

TL;DR

The paper investigates how masked predictive coding (MPC) during HuBERT pre-training shapes the type of information learned across speech-layer representations. It compares two MPL configurations with different layer placements and label counts, evaluating the resulting representations on nine SUPERB probing tasks via linearly weighted per-layer features. The main finding is that content information increases as MPC loss is minimized and is concentrated near the loss location, while speaker information tends to reside in earlier layers and cannot be directly controlled by MPC. These results inform the design of future pre-training criteria and tasks that aim to preserve both content and speaker information across layers, highlighting the limits of MPC alone for universal speech representation learning.

Abstract

Recent developments in pre-trained speech representation utilizing self-supervised learning (SSL) have yielded exceptional results on a variety of downstream tasks. One such technique, known as masked predictive coding (MPC), has been employed by some of the most high-performing models. In this study, we investigate the impact of MPC loss on the type of information learnt at various layers in the HuBERT model, using nine probing tasks. Our findings indicate that the amount of content information learned at various layers of the HuBERT model has a positive correlation to the MPC loss. Additionally, it is also observed that any speaker-related information learned at intermediate layers of the model, is an indirect consequence of the learning process, and therefore cannot be controlled using the MPC loss. These findings may serve as inspiration for further research in the speech community, specifically in the development of new pre-training tasks or the exploration of new pre-training criterion's that directly preserves both speaker and content information at various layers of a learnt model.
Paper Structure (12 sections, 1 figure, 1 table)

This paper contains 12 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Method
  4. Manipulating the impact of the masked prediction loss training criterion on the encoded information at various layers of HuBERT model.
  5. Evaluating the encoded information at different layers using probing tasks.
  6. Experimental details
  7. Results
  8. Impact of the MPC loss on the type of information learnt at various layers.
  9. Quantitative analysis of HuBERT_1 and HuBERT_3_0.4 performance on SUPERB benchmark
  10. Discussion
  11. Conclusion and future work
  12. Acknowledgments

Figures (1)

  • Figure 1: Weight analysis on eight different downstream tasks, a subset of SUPERB benchmark. X-axis represents different layer numbers. Layer 0 corresponds to the input of the first HuBERT encoder layer. Y-axis corresponds eight different tasks used for evaluation. To understand the naming convention, read through Section \ref{['sec:method-approach']}. Better viewed in color.