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Resonance-based decomposition for the manipulation of acoustic cues in speech: An assessment of perceived quality

机译:基于共振的分解,用于语音提示的处理:感知质量的评估

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The ultimate objective of this study is to employ a resonance-based decomposition method for the manipulation of acoustic cues in speech. Resonance-based decomposition (Selesnick, 2010) is a newly proposed nonlinear signal analysis method based not on frequency or scale but on resonance; the method is able to decompose a complex non-stationary signal into a ‘high-resonance’ component and a ‘low-resonance’ component using a combination of low- and high- Q-factors. In this study, we conducted a subjective listening experiment on five normal hearing listeners to assess the perceived quality of decomposed components, with the intention of deriving the perceptually relevant combinations of low- and high- Q-factors. Our results show that normal hearing listeners generally rank high-resonance components of speech stimuli higher than low-resonance components. This may be due to a greater salience of perceptually significant formant cues in high-resonance stimuli.
机译:这项研究的最终目的是采用基于共振的分解方法来处理语音中的声音提示。基于共振的分解(Selesnick,2010)是一种新提出的非线性信号分析方法,它不是基于频率或尺度而是基于共振。该方法能够结合使用低和高Q因子将复杂的非平稳信号分解为“高共振”分量和“低共振”分量。在这项研究中,我们对五个正常的听力听者进行了主观听觉实验,以评估分解成分的感知质量,以期得出低Q因子和高Q因子在感知上相关的组合。我们的结果表明,正常听力的听众通常将语音刺激的高共振分量排在低共振分量之上。这可能是由于在高共振刺激中感知上显着的共振峰提示更加突出。

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