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Minimal Representation of Speech Signals for Generation of Emotion Speech and Human-Robot Interaction

机译:言语发言的最小代表语音信号的言论和人体机器人互动

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In this paper minimal representation of voiced speech based on decomposition into AM-FM components is proposed for generation of emotion speech. For the decomposition, firstly time-frequency boundaries of AM-FM components are estimated and secondary each AM-FM component is extracted by using the variable bandwidth filter [17] adaptive to the estimated time-frequency boundaries. Finally, two parameters, that is, instantaneous frequency and instantaneous amplitude of each AM-FM component are estimated. The set composed of instantaneous amplitudes and instantaneous frequencies is the minimal representation of voiced speech signals. The minimal representation is optimal feature set since the set describes effectively the biomechanical characteristics of the vocal codes and the vocal track. Raw speech signals are modified by changing the parameters for generation of emotion speech.
机译:在本文中,提出了基于分解成AM-FM组件的浊音语音的最小表示,用于产生情感演讲。对于分解,估计AM-FM组件的首先时间频率边界并通过使用自适应到估计的时间频率边界来提取次级每个AM-FM组件。最后,估计两个参数,即每个AM-FM分量的瞬时频率和瞬时幅度。由瞬时振积和瞬时频率组成的集合是具有浊音语音信号的最小表示。最小表示是最佳特征集,因为该组有效地描述了声乐码和声道的生物力学特征。通过改变生成情感语音的参数来修改原始语音信号。

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