TY -的A2 -阿里,拉赫曼AU - Ma,他非盟-左,易盟-李,铁山盟——陈,c·l·菲利普PY - 2020 DA - 2020/06/29 TI -数据驱动的决策支持系统扬声器使用E-Vector系统识别SP - 4748606六世- 2020 AB -最近,使用指纹生物识别授权,声纹,面部特征引起了相当大的关注从公众与识别技术的发展和智能手机的普及。在这样的生物识别技术,声纹有个人身份高的指纹也使用非接触模式识别相似的面孔。语音信号处理是语音识别精度的关键之一。大多数语音识别系统仍然采用mel-scale频率倒谱系数(MFCC)发音特征的关键。MFCC的质量和准确性依赖于准备好的短语,属于text-dependent议长识别。相比之下,一些新的特性,如维矢量,提供一个黑箱过程声乐学习的特性。为了解决这些方面,小说的声音特征提取的数据驱动的方法提出了基于决策支持系统(DSS)。每个语音信号可以转换为一个向量表示使用该DSS声音的特性。建立这个DSS包括三个步骤:(i)语音数据预处理,(ii)分层聚类分析的逆离散余弦变换倒频谱系数,和(3)学习欧几里得度量的E-vector通过最小化。我们对比实验来验证E-vectors提取DSS与其他声音特性措施,并将它们应用于text-dependent和text-independent数据集。 In the experiments containing one utterance of each speaker, the average accuracy of the E-vector is improved by approximately 1.5% over the MFCC. In the experiments containing multiple utterances of each speaker, the average micro-F1 score of the E-vector is also improved by approximately 2.1% over the MFCC. The results of the E-vector show remarkable advantages when applied to both the Texas Instruments/Massachusetts Institute of Technology corpus and LibriSpeech corpus. These improvements of the E-vector contribute to the capabilities of speaker identification and also enhance its usability for more real-world identification tasks. SN - 1058-9244 UR - https://doi.org/10.1155/2020/4748606 DO - 10.1155/2020/4748606 JF - Scientific Programming PB - Hindawi KW - ER -