Interspeech 2021 2021
DOI: 10.21437/interspeech.2021-1710
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Self-Supervised End-to-End ASR for Low Resource L2 Swedish

Abstract: Unlike traditional (hybrid) Automatic Speech Recognition (ASR), end-to-end ASR systems simplify the training procedure by directly mapping acoustic features to sequences of graphemes or characters, thereby eliminating the need for specialized acoustic, language, or pronunciation models. However, one drawback of end-to-end ASR systems is that they require more training data than conventional ASR systems to achieve similar word error rate (WER). This makes it difficult to develop ASR systems for tasks where tran… Show more

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Cited by 5 publications
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