2022
DOI: 10.48550/arxiv.2201.11207
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Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition

Abstract: The high cost of data acquisition makes Automatic Speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a written script, or for which the phone inventories remain unknown. Past works explored multilingual training, transfer learning, as well as zero-shot learning in order to build ASR systems for these low-resource languages. While it has been shown that the pooling of resources from multiple languages is helpful, we have not yet seen a succ… Show more

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