On the Cross-lingual Transferability of Pre-trained wav2vec2-based Models

TL;DR

This study investigates how well wav2vec 2.0-based models transfer knowledge across different languages, revealing that data diversity and language similarity influence cross-lingual speech recognition performance.

Contribution

It provides a comprehensive analysis of cross-lingual transferability of wav2vec 2.0 models across 18 languages, highlighting the importance of data diversity and language similarity.

Findings

Performance is more affected by data diversity than data size during pre-training.

Indo-European languages outperform non-Indo-European languages in transfer tasks.

Positive transfer observed across all languages, especially when pre-training language is similar to target language.

Abstract

Using representations provided by a large pre-trained model has become the primary strategy for achieving state-of-the-art results in a wide range of tasks. A recently proposed large pre-trained model, wav2vec 2.0, was seminal for several other works on pre-training large models on speech data. Many models are being pre-trained using the same architecture as wav2vec 2.0 and are getting state-of-the-art in various speech-related tasks. Previous work has demonstrated that the data used during the pre-training of these wav2vec2-based models can impact the model's performance in downstream tasks, and this should be taken into consideration before utilizing these models. However, few works have proposed investigating further how the transfer knowledge of these pre-trained models behaves in different languages, even when the target language differs from the one used during the model's pre-training. Our work aims to investigate the cross-lingual transferability of these wav2vec2-based models. We performed several fine-tuning experiments on the speech recognition task in 18 languages using 15 large pre-trained models. The results of our experiments showed us that the size of data used during the pre-training of these models is not as important to the final performance as the diversity. We noticed that the performance of Indo-European languages is superior to non-Indo-European languages in the evaluated models. We have observed a positive cross-lingual transfer of knowledge using monolingual models, which was evident in all the languages we used, but more pronounced when the language used during pre-training was more similar to the downstream task language. With these findings, we aim to assist the scientific community in utilizing existing wav2vec2-based pre-trained models, as well as facilitate the pre-training of new ones.