End-to-end attention-based distant speech recognition with Highway LSTM

Hassan Taherian

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arXiv · 1610.05361

End-to-end attention-based distant speech recognition with Highway LSTM

Hassan Taherian

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Abstract

End-to-end attention-based models have been shown to be competitive alternatives to conventional DNN-HMM models in the Speech Recognition Systems. In this paper, we extend existing end-to-end attention-based models that can be applied for Distant Speech Recognition (DSR) task. Specifically, we propose an end-to-end attention-based speech recognizer with multichannel input that performs sequence prediction directly at the character level. To gain a better performance, we also incorporate Highway long short-term memory (HLSTM) which outperforms previous models on AMI distant speech recognition task.

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BibTeX RIS

Hassan Taherian. 2016-10-17. End-to-end attention-based distant speech recognition with Highway LSTM. https://arxiv.org/abs/1610.05361

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