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End-to-end automatic speech recognition (ASR) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (WER). This suggests that predicting sequences of words directly may be helpful instead. However, training with word-level supervision can be more difficult due to the sparsity of examples per label class. In this paper, we analyze an end-to-end ASR model that combines a word-and-character representation in a multi-task learning (MTL) framework. We show that it improves on the WER and study how the word-level model can benefit from character-level supervision by analyzing the learned inductive preference bias of each model component empirically. We find that by adding character-level supervision, the MTL model interpolates between recognizing more frequent words (preferred by the word-level model) and shorter words (preferred by the character-level model). Keywords: speech recognition, multi-task learning, interpretability.
Author Information
Jan Kremer (Corti)
Jan Kremer is a senior machine learning researcher at Corti in Denmark. There he works on automatic speech recognition in critical conversations. He is particularly interested in multi-task and robust learning. After receiving an MSc in Computer Science from the Technical University of Munich in 2013, he obtained a PhD in Machine Learning from the University of Copenhagen in 2016.
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