26,374 research outputs found
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection
Speech recognition systems have achieved high recognition performance for
several tasks. However, the performance of such systems is dependent on the
tremendously costly development work of preparing vast amounts of task-matched
transcribed speech data for supervised training. The key problem here is the
cost of transcribing speech data. The cost is repeatedly required to support
new languages and new tasks. Assuming broad network services for transcribing
speech data for many users, a system would become more self-sufficient and more
useful if it possessed the ability to learn from very light feedback from the
users without annoying them. In this paper, we propose a general reinforcement
learning framework for speech recognition systems based on the policy gradient
method. As a particular instance of the framework, we also propose a hypothesis
selection-based reinforcement learning method. The proposed framework provides
a new view for several existing training and adaptation methods. The
experimental results show that the proposed method improves the recognition
performance compared to unsupervised adaptation.Comment: 5 pages, 6 figure
Deep Learning: Our Miraculous Year 1990-1991
In 2020, we will celebrate that many of the basic ideas behind the deep
learning revolution were published three decades ago within fewer than 12
months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich.
Back then, few people were interested, but a quarter century later, neural
networks based on these ideas were on over 3 billion devices such as
smartphones, and used many billions of times per day, consuming a significant
fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201
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