Document Type

Conference Proceeding

Publication Title

ICNLSP 2022 - Proceedings of the 5th International Conference on Natural Language and Speech Processing


In speech recognition, it is essential to model the phonetic content of the input signal while discarding irrelevant factors such as speaker variations and noise, which is challenging in low-resource settings. Self-supervised pretraining has been proposed as a way to improve both supervised and unsupervised speech recognition, including frame-level feature representations and Acoustic Word Embeddings (AWE) for variable-length segments. However, self-supervised models alone cannot learn perfect separation of the linguistic content as they are trained to optimize indirect objectives. In this work, we experiment with different pre-trained self-supervised features as input to AWE models and show that they work best within a supervised framework. Models trained on English can be transferred to other languages with no adaptation and outperform self-supervised models trained solely on the target languages. © ICNLSP 2022.All rights reserved

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Linguistics, Natural language processing systems, Speech recognition, Acoustic word embedding, Embeddings, Feature representation, Learn+, Low-resource settings, Pre-training, Speaker variation, Transfer learning, Unsupervised ASR, Variable-length segments, Embeddings


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License: CC BY 4.0

Uploaded 29 November 2023