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
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
基于语音识别设计了针对脑瘫患儿的数字语音训练系统。应用人机交互(HRI)技术与仿生机械手动作控制相结合达到提升脑瘫(CP)康复训练效果的目的。该系统中控制器采用Arduino MEGA 2560为主控制器,显示屏LCD1602作为人机交互数据显示界面...基于语音识别设计了针对脑瘫患儿的数字语音训练系统。应用人机交互(HRI)技术与仿生机械手动作控制相结合达到提升脑瘫(CP)康复训练效果的目的。该系统中控制器采用Arduino MEGA 2560为主控制器,显示屏LCD1602作为人机交互数据显示界面,通过LD3320语音芯片实现人机语音交互功能。人机交互功能是通过Labview环境展开,可实现人、机器人的手势与动作的实时交互训练与评价。该系统可训练脑瘫患者反应能力、语言表述能力以及认识手势动作数字动作能力,为提升脑瘫康复训练系统提供关键技术。展开更多
基于Matlab设计了以动态时间规整(dynamic time warping,DTW)算法和Mel频率倒谱系数(Mel frequency cepstrum coefficients,MFCC)参数提取算法为基础的孤立词自动语音识别系统;然后结合机器人语音识别的需求,基于凌阳SPCE061A设计了语...基于Matlab设计了以动态时间规整(dynamic time warping,DTW)算法和Mel频率倒谱系数(Mel frequency cepstrum coefficients,MFCC)参数提取算法为基础的孤立词自动语音识别系统;然后结合机器人语音识别的需求,基于凌阳SPCE061A设计了语音识别应用系统。结合上述两部分工作,设计、实现了机器人语音识别系统演示实验和机器人语音识别半开放实验,实现效果达到预期实验设计目标。展开更多
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.
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。
文摘基于语音识别设计了针对脑瘫患儿的数字语音训练系统。应用人机交互(HRI)技术与仿生机械手动作控制相结合达到提升脑瘫(CP)康复训练效果的目的。该系统中控制器采用Arduino MEGA 2560为主控制器,显示屏LCD1602作为人机交互数据显示界面,通过LD3320语音芯片实现人机语音交互功能。人机交互功能是通过Labview环境展开,可实现人、机器人的手势与动作的实时交互训练与评价。该系统可训练脑瘫患者反应能力、语言表述能力以及认识手势动作数字动作能力,为提升脑瘫康复训练系统提供关键技术。
文摘基于Matlab设计了以动态时间规整(dynamic time warping,DTW)算法和Mel频率倒谱系数(Mel frequency cepstrum coefficients,MFCC)参数提取算法为基础的孤立词自动语音识别系统;然后结合机器人语音识别的需求,基于凌阳SPCE061A设计了语音识别应用系统。结合上述两部分工作,设计、实现了机器人语音识别系统演示实验和机器人语音识别半开放实验,实现效果达到预期实验设计目标。
