基于LPC、CC、MFCC算法的说话人识别系统的比较评价

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY
Yesenia González, H. Juárez, O. Rocha, R. P. Hernández, A. Bermúdez
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引用次数: 0

摘要

本文提出了基于LPC(线性预测编码),CC(倒谱系数)和MFCC (Mel频率倒谱系数)算法的说话人识别系统的评估,用于提取语音参数。评估遵循实验定量方法,包括确定输入信号暴露于不同噪声条件(人群和高斯噪声)时的性能变化,即在不同的信噪比水平下,比较2个扬声器的验证结果。尽管所有系统在噪声环境下的性能都会下降,但每个系统本质上都具有一定的鲁棒性。该评价将作为构建包括声音增强系统以降低噪声的说话人识别系统的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluación comparativa de sistemas de reconocimiento de locutor basados en los algoritmos LPC, CC y MFCC
This document proposes the evaluation of speaker recognition systems based on the LPC (Linear Predicting Coding), CC (Cepstral Coefficients) and MFCC (Mel Frequency Cepstral Coefficients) algorithms, used in the extraction of voice parameters. The evaluation, following an experimental quantitative methodology, consists of determining the change in performance when the input signal is exposed to different noise conditions (crowd and Gaussian noise), namely, at different levels of SNR, comparing the verification results for 2 speakers. Although all the systems decrease their performance in noisy environments, each one possesses intrinsically a certain level of robustness. This evaluation will serve as a reference in the construction of speaker recognition systems, which include voice enhancement systems to reduce noise.
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来源期刊
Memoria Investigaciones en Ingenieria
Memoria Investigaciones en Ingenieria ENGINEERING, MULTIDISCIPLINARY-
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