Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic

LREC 2020 Yonas Woldemariam

While building automatic speech recognition (ASR) requires a large amount of speech and text data, the problem gets worse for less-resourced languages. In this paper, we investigate a model adaptation method, namely transfer learning for a less-resourced Semitic language i.e., Amharic, to solve resource scarcity problems in speech recognition development and improve the Amharic ASR model... (read more)

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