Multilingual acoustic word embedding models for processing zero-resource languages

• URL: http://arxiv.org/abs/2002.02109v2
• Date: Fri, 21 Feb 2020 14:19:56 GMT
• Title: Multilingual acoustic word embedding models for processing zero-resource languages
• Authors: Herman Kamper, Yevgen Matusevych, Sharon Goldwater
• Abstract summary: We train a single supervised embedding model on labelled data from multiple well-resourced languages. We then apply it to unseen zero-resource languages.
• Score: 37.78342106714364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more on the particular training--testing language pair.

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