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An Age Adapting Electrolarynx - A Feasibility Study

机译:一种适应ElectrolaryNX的年龄 - 可行性研究

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We propose a mathematical model for voice aging that could be used in the design of an age-adapting Electrolarynx. Voice data from public figures, at the ages of 30, 40, 50 and 60 years old, were acquired from a YouTube corpus. The voice processing consisted of an extraction of 70 Mel-Frequency Cepstral Coefficients (MFCCs) and a computation of their statistical features. ANOVA F-tests were used to determine which of these features change with age. Significant differences between age groups were found only for the first 40 MFCCs. The aging model was then constructed using non-linear regression and an averaged quadratic polynomial fit on these coefficients. Model age-adapted voices were reconstructed from the young dataset speakers' voices and compared to their voices at older ages. The model was validated by the correlation between speakers' MFCCs at older ages and the model-aged MFCCs. The average correlation results were in the range of 0.62 to 0.93. The results imply that the first 40 MFCCs are more susceptible to age related changes and that the proposed model has the potential to enhance the Electrolarynx by providing age adaptation as the speaker grows older.
机译:我们提出了一种用于语音老化的数学模型,可用于设计年龄适配的Electrolalarynx。来自公众数据的语音数据,在30,40,50和60岁的年龄,从YouTube语料库中获得。语音处理由70念珠菌谱系数(MFCC)的提取和其统计特征的计算组成。 ANOVA F-Tests用于确定这些功能随年龄变化的变化。仅针对前40名MFCCS发现年龄组之间的显着差异。然后使用非线性回归和在这些系数上平均二次多项式拟合来构建老化模型。模型年龄适应的声音从年轻的数据集发言人的声音重建,并与他们的老年人的声音相比。通过较旧的年龄和模型 - 老化的MFCCs之间的扬声器的MFCC之间的相关性验证了该模型。平均相关结果在0.62至0.93的范围内。结果意味着前40 MFCCs更容易受到年龄相关变化的影响,并且所提出的模型具有通过提供年龄适应随着扬声器变老而增强电器的潜力。

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