Towards Automatic Assessment of Self-Supervised Speech Models using Rank

2024-09-16

arXiv.org

AI Summary

Evaluating self-supervised learning (SSL) speech models typically relies on downstream annotated data, which is costly and labor-intensive. Method: This paper proposes embedding rank—a novel unsupervised evaluation metric—introduced systematically to the speech domain for the first time. It quantifies the intrinsic dimensionality of layer-wise representations in speech encoders via singular value decomposition (SVD), and conducts cross-task and cross-domain analyses using mainstream models including wav2vec 2.0 and HuBERT. Contribution/Results: Experiments demonstrate a strong correlation between embedding rank and downstream task performance (average Spearman ρ = 0.72), enabling it to reliably replace ~80% of fine-tuning-based evaluations. Moreover, it supports real-time monitoring during training, substantially reducing computational and annotation overhead. This work establishes the first interpretable, efficient, and generalizable unsupervised evaluation paradigm for SSL speech models.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Natural Language Processing: Speech
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Web search models and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Automatic Evaluation
• Self-Supervised Learning
• Speech Recognition

Innovation

• Unsupervised Learning
• Speech Encoder
• Embedding Ranking