Leveraging Discrete Speech Units for Compact Speech Translation Models

Core Concepts

The author proposes a method to distill knowledge from large SSL models using DSU pretraining to create more compact ST models, highlighting benefits over using DSU as model inputs.
Our method avoids lengthy inference pipelines and shows robustness over tokenization, making it suitable for low-resource settings.

Abstract

The content discusses leveraging Self-Supervised Learning (SSL) models for Speech Translation (ST) by pretraining smaller models on Discrete Speech Units (DSU). The method aims to distill knowledge from large SSL models to create more compact ST models, showcasing advantages over traditional methods like ASR pretraining. Evaluation results demonstrate the effectiveness of the proposed approach in improving BLEU scores and robustness across different tokenizations.
The authors detail the process of DSU pretraining and finetuning on paired ST data, emphasizing the use of CTC regularization to enhance performance. Results show significant improvements in BLEU scores compared to baseline models, with DSU-Adapter outperforming Hu-Transformer and ASR Pretraining in various language pairs and resource levels.
Key points include data preprocessing steps, model configurations, training and inference details, as well as an analysis of results based on chrF and COMET scores. The study also explores the impact of CTC regularization on both pretraining and finetuning stages, highlighting its positive effect on model performance.

Stats

Our method is >0.5 BLEU better than a ST model that directly finetunes the SSL model.
Our method is also >0.5 BLEU better than a ST model that directly finetuned HuBERT despite having half model size.
DSU-Adapter is 3 BLEU points higher than Scratch.
DSU-Adapter is more tokenization robust compared to DSU-to-Trl model.

Quotes

"Our method avoids lengthy inference pipelines in the DSU-to-Trl method."
"Our method requires less components in inference and shows stronger robustness over tokenization."

Key Insights Distilled From

Compact Speech Translation Models via Discrete Speech Units Pretraining

by Tsz Kin Lam,... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19333.pdf

Deeper Inquiries

How can K-Means clustering parameters affect the quality of DSUs obtained

The quality of Discrete Speech Units (DSUs) obtained through K-Means clustering can be significantly impacted by the parameters used in the clustering process. The key parameters that can affect the quality of DSUs include:

Number of Clusters (K): The choice of the number of clusters directly influences how well speech representations are grouped into distinct units. A higher value of K may lead to more granular and specific DSUs, while a lower value may result in oversimplified or generalized units.
Initialization Method: Different initialization methods for centroids in K-Means clustering, such as random initialization or k-means++, can impact convergence speed and final cluster assignments, ultimately affecting DSU quality.
Convergence Criteria: The criteria used to determine when the algorithm has converged plays a crucial role in ensuring that meaningful DSUs are obtained without unnecessary iterations that could degrade unit quality.

Optimizing these parameters through experimentation and fine-tuning is essential to ensure high-quality DSUs for effective pretraining and subsequent model performance improvement.

What other acoustic encoders could be experimented with for further gains in the proposed method

In addition to HuBERT, other acoustic encoders could be experimented with for further gains in the proposed method. Some potential options include:

Conformer: Conformer models have shown promising results in various speech-related tasks due to their ability to capture long-range dependencies efficiently.
E-Branchformer: This encoder-decoder architecture combines transformer-based modeling with branch mechanisms for enhanced performance on speech recognition and translation tasks.
Other SSL Models: Exploring different layers from self-supervised learning models like wav2vec 2.0 or other variants could provide diverse representations for extracting high-quality DSUs.

By experimenting with these alternative acoustic encoders, researchers can assess their effectiveness in generating informative DSUs and potentially improving overall model compactness and performance.

How might utilizing other layers from SSL models impact the quality of extracted DSUs

Utilizing different layers from SSL models beyond just the 6th layer could have varying impacts on the quality of extracted Discrete Speech Units (DSUs). Here are some potential effects:

Higher Layers: Layers closer to output tend to capture more abstract features relevant for downstream tasks like speech translation, potentially leading to more semantically rich DSU representations.

2Lower Layers: Lower layers often encode more low-level information about phonetic characteristics or acoustic properties which might be beneficial if fine-grained distinctions are needed within generated units.
Experimenting with multiple layers from SSL models allows researchers to explore a range of abstraction levels present in speech representations, enabling them to select optimal layers based on task requirements for enhancing model efficiency and effectiveness during pretraining processes