Multilingual Transfer Learning for Children Automatic Speech Recognition

Thomas Rolland, Alberto Abad, Catia Cucchiarini, Helmer Strik


Abstract
Despite recent advances in automatic speech recognition (ASR), the recognition of children’s speech still remains a significant challenge. This is mainly due to the high acoustic variability and the limited amount of available training data. The latter problem is particularly evident in languages other than English, which are usually less-resourced. In the current paper, we address children ASR in a number of less-resourced languages by combining several small-sized children speech corpora from these languages. In particular, we address the following research question: Does a novel two-step training strategy in which multilingual learning is followed by language-specific transfer learning outperform conventional single language/task training for children speech, as well as multilingual and transfer learning alone? Based on previous experimental results with English, we hypothesize that multilingual learning provides a better generalization of the underlying characteristics of children’s speech. Our results provide a positive answer to our research question, by showing that using transfer learning on top of a multilingual model for an unseen language outperforms conventional single language-specific learning.
Anthology ID:
2022.lrec-1.795
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
7314–7320
Language:
URL:
https://aclanthology.org/2022.lrec-1.795
DOI:
Bibkey:
Cite (ACL):
Thomas Rolland, Alberto Abad, Catia Cucchiarini, and Helmer Strik. 2022. Multilingual Transfer Learning for Children Automatic Speech Recognition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 7314–7320, Marseille, France. European Language Resources Association.
Cite (Informal):
Multilingual Transfer Learning for Children Automatic Speech Recognition (Rolland et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.795.pdf