传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音...传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音信号翻译机器人的语义纠错模型,采用随时间反向传播(Backpropagation Through Time,BPTT)循环训练核验方式,以确保纠错的准确性。测试结果显示,经过3个阶段测试,选定的5段语音材料的纠错识别率成功控制在10%以下,表明基于语音信号的跨语种交互翻译机器人语义纠错方法高效,具有实际应用价值。展开更多
A radical new approach is presented to programming human-like levels of Artificial Intelligence (AI) into a humanoid robot equipped with a verbal-phoneme sound generator. The system shares 3 important characteristics ...A radical new approach is presented to programming human-like levels of Artificial Intelligence (AI) into a humanoid robot equipped with a verbal-phoneme sound generator. The system shares 3 important characteristics with human-like input data and processing: 1) The raw data and preliminary processing of the raw data are human-like. 2) All the data are subjective, that is related and correlated with a robotic self-identity coordinate frame. 3) All the data are programmed behaviorally into the system. A multi-tasking Relational Robotic Controller (RRC)-Humanoid Robot, described and published in the peer-reviewed literature, has been specifically designed to fulfill those 3 characteristics. A RRC-controlled system may be behaviorally programmed to achieve human-like high I.Q. levels of subjective AI for the visual signals and the declarative-verbal words and sentences heard by the robot. A proof of concept RRC-Humanoid Robot is under development and present status is presented at the end of the paper.展开更多
文摘传统的跨语种交互翻译机器人语义纠错方法通常是单向的,效率较低,导致识别错误率较高。为此,文章提出基于语音信号的跨语种交互翻译机器人语义纠错方法。在基础语音识别的基础上,通过交互标定和特征提取来修正语义错误位置,并设计语音信号翻译机器人的语义纠错模型,采用随时间反向传播(Backpropagation Through Time,BPTT)循环训练核验方式,以确保纠错的准确性。测试结果显示,经过3个阶段测试,选定的5段语音材料的纠错识别率成功控制在10%以下,表明基于语音信号的跨语种交互翻译机器人语义纠错方法高效,具有实际应用价值。
文摘A radical new approach is presented to programming human-like levels of Artificial Intelligence (AI) into a humanoid robot equipped with a verbal-phoneme sound generator. The system shares 3 important characteristics with human-like input data and processing: 1) The raw data and preliminary processing of the raw data are human-like. 2) All the data are subjective, that is related and correlated with a robotic self-identity coordinate frame. 3) All the data are programmed behaviorally into the system. A multi-tasking Relational Robotic Controller (RRC)-Humanoid Robot, described and published in the peer-reviewed literature, has been specifically designed to fulfill those 3 characteristics. A RRC-controlled system may be behaviorally programmed to achieve human-like high I.Q. levels of subjective AI for the visual signals and the declarative-verbal words and sentences heard by the robot. A proof of concept RRC-Humanoid Robot is under development and present status is presented at the end of the paper.
