3 accepted papers
In multi-channel far-field automatic speech recognition (ASR) scenarios, distortion is introduced when the speech signal is processed by the front end, which damages the recognition performance for the ASR tasks. In this paper, we propose a dual-path network for the far-field acoustic model, which u…
In this work, we try to answer two questions: Can deeply learned features with discriminative power benefit an ASR system’s robustness to acoustic variability? And how to learn them without requiring framewise labelled sequence training data? As existing methods usually require knowing where the lab…
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of s…