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Speech Recognition, Machine Translation, and Speech Translation—A Unified Discriminative Learning Paradigm [Lecture Notes]

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2 Author(s)
Xiaodong He ; At Microsoft Res., Redmond, WA, USA ; Li Deng

In the past two decades, significant progress has been made in automatic speech recognition (ASR) [2], [9] and statistical machine translation (MT) [12]. Despite some conspicuous differences, many problems in ASR and MT are closely related and techniques in the two fields can be successfully cross-pollinated. In this lecture note, we elaborate on the fundamental connections between ASR and MT, and show that the unified ASR discriminative training paradigm recently developed and presented in [7] can be extended to train MT models in the same spirit.

Published in:

Signal Processing Magazine, IEEE  (Volume:28 ,  Issue: 5 )