Human Language Acquisition methods in a Machine Learning Task

机译：机器学习任务中的人类语言习得方法

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The goal of this study is to develop a psycho-computational model of human phoneme acquisition that includes the knowledge of linguistic universals to "teach" Artificial Neural Nets incrementally. Long Short-Term Memory (LSTM) artificial neural networks are capable to outperform previous recurrent networks on many tasks ranging from grammar recognition to speech and robot control. Together with our psycho-computational model they are supposed to recognize phonetic features in a way similar to humans learning to understand their first language.

机译：这项研究的目的是开发一种人类语音学习的心理计算模型，其中包括语言通用知识，以“逐步”教授“人工神经网络”。长短期记忆（LSTM）人工神经网络在从语法识别到语音和机器人控制等许多任务上都能够胜过以前的递归网络。他们应该与我们的心理计算模型一起，以类似于人类学习理解母语的方式来识别语音特征。

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《International Conference on Spoken Language Processing; 20041004-08; Jeju(KR)》|2004年|P.2233-2236|共4页
会议地点 Jeju(KR)
作者
Nicole Beringer;
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IDSIA: Dalle Molle Institute for Artificial Intelligence Galleria 2, 6928 Manno-Lugano, Switzerland;

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原文格式 PDF
正文语种 eng
中图分类应用语言学;
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Human Language Acquisition methods in a Machine Learning Task

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