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Speech recognition algorithm for natural language management systems under variety of accents

机译：自然语言管理系统的语音识别算法

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This paper proposes a concept of a new approach to the development of speech recognition systems using multi-agent neurocognitive modeling. The fundamental foundations of these developments are based on the theory of cognitive psychology and neuroscience, and advances in computer science. The purpose of this work is the development of general theoretical principles of sound image recognition by an intelligent robot and, as the sequence, the development of a universal system of automatic speech recognition, resistant to speech variability, not only with respect to the individual characteristics of the speaker, but also with respect to the diversity of accents. Based on the analysis of experimental data obtained from behavioral studies, as well as theoretical model ideas about the mechanisms of speech recognition from the point of view of psycholinguistic knowledge, an algorithm resistant to variety of accents for machine learning with imitation of the formation of a person’s phonemic hearing has been developed.
机译：本文提出了一种新方法的概念，使用多毒者神经认知建模的语音识别系统的发展。这些发展的基本基础是基于认知心理学和神经科学的理论，以及计算机科学的进步。这项工作的目的是通过智能机器人开发声像识别的通用理论原则，作为序列，开发通用语音识别的通用系统，抵抗语音变异性，不仅相对于各个特征扬声器，也是关于口音的多样性。基于对行为研究中获得的实验数据的分析，以及从精神语言知识的角度来看，语音识别机制的理论模型思想，仿旧机器学习各种抗性算法已经开发了人的音素听证。

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