Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models

• 作者: Alfredo Maesa Fabio Garzia Michele Scarpiniti Roberto Cusani
• 来源期刊: 信息安全(英文)
• 学科: 工学
• 关键词: AUTOMATIC SPEAKER RECOGNITION Access Control VOICE RECOGNITION BIOMETRICS
• 年，卷（期）: 2012,（4）
• 页码范围: 335-340
• 页数: 6页
• 分类号: TP39

摘要

The aim of this paper is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture Models (GMM), in order to develop a security control access gate. 450 speakers were randomly extracted from the Voxforge.org audio database, their utterances have been improved using spectral subtraction, then MFCC were extracted and these coefficients were statistically analyzed by GMM in order to build each profile. For each speaker two different speech files were used: the first one to build the profile database, the second one to test the system performance. The accuracy achieved by the proposed approach is greater than 96% and the time spent for a single test run, implemented in Matlab language, is about 2 seconds on a common PC.