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A new pitch-range based feature set for a speaker's age and gender classification

机译：一种基于音高范围的新功能集，可用于讲话者的年龄和性别分类

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摘要

This paper presents a pitch-range (PR) based feature set for age and gender classification. The performance of the proposed feature set is compared With MFCCs, energy, relative spectral transform-perceptual linear prediction (RASTA_PLP), and fundamental frequency (F0). Voice activity detection (VAD) is performed to extract speech utterances before feature extraction. Two different classifiers, k-Nearest Neighbors (kNN) and Support Vector Machines (SVM) are used in order to evaluate the effectiveness of the feature sets. Experimental results are reported for the aGender database. Both kNN and SVM classifiers achieved the highest accuracy rates by the proposed PR feature set in age + gender and age classifications. PR features represent the pitch changes over time. In age + gender classification, the class of middle-aged female speaker is recognized with an accuracy of 92.86%, followed by senior female speakers with 83.61%, children with 83.02%, middle-aged male speakers with 73.58%, young female speakers with 67.35%, and senior male speakers with 34.33% by using 3PR features with the SVM classifier. Low classification accuracies are observed for young male speakers. (C) 2015 Elsevier Ltd. All rights reserved.
机译：本文提出了基于音高范围（PR）的年龄和性别分类功能集。将所提出的功能集的性能与MFCC，能量，相对频谱变换-感知线性预测（RASTA_PLP）和基本频率（F0）进行了比较。执行语音活动检测（VAD）以在特征提取之前提取语音。为了评估特征集的有效性，使用了两个不同的分类器，即k最近邻（kNN）和支持向量机（SVM）。报告了aGender数据库的实验结果。通过建议的年龄，性别和年龄分类的PR功能集，kNN和SVM分类器均达到了最高的准确率。 PR功能代表音高随时间的变化。在年龄+性别分类中，中年女性说者的识别率为92.86％，其次是高级女性说者（83.61％），儿童（83.02％），中年男性（73.58％），年轻女性（女）。通过SVM分类器使用3PR功能，可以达到67.35％的高级男性演讲者和34.33％的高级男性演讲者。对于年轻的男性说话者，分类准确性较低。 （C）2015 Elsevier Ltd.保留所有权利。

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