哪种输入抽象更适合机器人语法获取模型?音素、单词还是语法结构?

Xavier Hinaut
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引用次数: 7

摘要

最近几年,语音识别系统取得了相当大的进展。随着深度学习方法的出现,单词识别的错误率下降了。然而,如果使用基于云的语音API并将其集成到机器人架构[33]中,仍然会遇到大量错误句子识别的情况。因此,语音识别不能被认为是解决了,特别是当一个话语是孤立地考虑其上下文。必须找到能够适应不同人机交互应用程序和上下文的特定解决方案。从这个角度来看,儿童学习语言的方式以及我们大脑处理话语的方式可能有助于我们改进机器人处理语言的方式。从语言习得理论和大脑如何处理句子中获得灵感,我们之前开发了一个神经启发的句子处理模型。在本研究中,我们研究了该模型如何处理不同层次的抽象输入:音素序列、单词序列或语法结构。我们看到,即使这个模型之前只测试过语法结构,它在单词和音素输入方面也有更好的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Which Input Abstraction is Better for a Robot Syntax Acquisition Model? Phonemes, Words or Grammatical Constructions?
There has been a considerable progress these last years in speech recognition systems [13]. The word recognition error rate went down with the arrival of deep learning methods. However, if one uses cloud-based speech API and integrates it inside a robotic architecture [33], one still encounters considerable cases of wrong sentences recognition. Thus speech recognition can not be considered as solved especially when an utterance is considered in isolation of its context. Particular solutions, that can be adapted to different Human-Robot Interaction applications and contexts, have to be found. In this perspective, the way children learn language and how our brains process utterances may help us improve how robot process language. Getting inspiration from language acquisition theories and how the brain processes sentences we previously developed a neuro-inspired model of sentence processing. In this study, we investigate how this model can process different levels of abstractions as input: sequences of phonemes, sequences of words or grammatical constructions. We see that even if the model was only tested on grammatical constructions before, it has better performances with words and phonemes inputs.
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