使用mel频率倒频谱系数和平方和误差识别说话人

Atik Charisma, M. R. Hidayat, Y. Zainal
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引用次数: 14

摘要

Mel-Frequency倒谱系数矢量量化方法(MFCC-VQ)可用于说话人验证系统。利用Mel频率倒谱系数(MFCC)向量对语音信号进行特征提取,得到声学语音信号。矢量量化(VQ)用于形成每个扬声器的特定声学矢量。引入或验证,Sum Square Error使用最小误差将未识别的说话人与文件库中的说话人进行匹配。在本研究中,使用该系统来验证说话人,即印尼语中的红、蓝、绿。本系统通过对比声源的成功率进行了测试,音箱验证被用作文件库和对声源建模说不用作文件库。从每次发音测试20次的成功率中得到了良好的说话者验证。对与文件库相同的说话人进行测试,平均验证成功率为70%,而对未作为文件库的说话人进行测试,平均验证成功率为83.3%。
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Speaker recognition using mel-frequency cepstrum coefficients and sum square error
The Method of Mel-Frequency Cepstral Coefficients Vector Quantization (MFCC-VQ) can be used in the speaker verification system. The process of feature extraction of speech signal using Mel Frequency Cepstral Coefficients (MFCC) vectors will produce acoustic speech signal. Vector quantization (VQ) is used to form the specific acoustic vector for each speaker. The introduction or verification, Sum Square Error is used to match unidentified speakers with speakers in filebase by the smallest error. In this research, the system is used to verify the speaker, namely red, blue, and green in Indonesian. This system has been tested by comparing the success rates between sound source speaker verification are used as filebase and modeling to the sound source said that is not used as filebase. From 20 times the pronunciation of each test the percentage of success obtained a good speaker verification. On testing the speaker with the same as filebase, the average percentage of verification success was 70%, while testing the speaker that are not used as filebase obtain an average percentage of 83.3% successful verification.
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