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A New Algorithm for Auditory Feature Extraction

机译:一种用于听觉特征提取的新算法

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摘要

The human auditory system possesses remarkable capabilities to analyze and identify signals. An auditory feature can improve the performance of speaker recognition system. In this paper, it used gammatone filter to model auditory system, and extracted a traditional auditory feature based on logarithmic energy. On the purpose to reduce the dimension of the traditional auditory feature which was always a highdimensional feature, we took use of discrete cosine transform (DCT). Therefore, it was feasible to apply auditory model extracted in a new way to speech feature extraction. The experiments showed that the GFCC feature, which was extracted in the new method, performed better than the traditional MFCC feature in the speaker recognition system based on GMM model.
机译:人类听觉系统具有显着的能力来分析和识别信号。 听觉功能可以提高扬声器识别系统的性能。 在本文中,它使用伽马酸胶过滤器来模拟听觉系统,并根据对数能提取传统听觉特征。 目的是减少传统听觉特征的维度,始终是一个高度的特征,我们采用了离散余弦变换(DCT)。 因此,在语音特征提取的新方法中应用听觉模型是可行的。 该实验表明,在新方法中提取的GFCC特征比基于GMM模型的扬声器识别系统中的传统MFCC特征更好地执行。

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