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Super-Human Performance in Online Low-latency Recognition of Conversational Speech
Achieving super-human performance in recognizing human speech has been a...
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ELITR Non-Native Speech Translation at IWSLT 2020
This paper is an ELITR system submission for the non-native speech trans...
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Relative Positional Encoding for Speech Recognition and Direct Translation
Transformer models are powerful sequence-to-sequence architectures that ...
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High Performance Sequence-to-Sequence Model for Streaming Speech Recognition
Recently sequence-to-sequence models have started to achieve state-of-th...
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Low Latency ASR for Simultaneous Speech Translation
User studies have shown that reducing the latency of our simultaneous le...
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Toward Cross-Domain Speech Recognition with End-to-End Models
In the area of multi-domain speech recognition, research in the past foc...
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Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation
Sequence-to-Sequence (S2S) models recently started to show state-of-the-...
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Very Deep Self-Attention Networks for End-to-End Speech Recognition
Recently, end-to-end sequence-to-sequence models for speech recognition ...
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Learning Shared Encoding Representation for End-to-End Speech Recognition Models
In this work, we learn a shared encoding representation for a multi-task...
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Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models
Acoustic-to-word (A2W) models that allow direct mapping from acoustic si...
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