文章基本信息

标题：FPGA Implementation for GMM-Based Speaker Identification
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作者：Phaklen EhKan ; Timothy Allen ; Steven F. Quigley 等
期刊名称：International Journal of Reconfigurable Computing
印刷版ISSN：1687-7195
电子版ISSN：1687-7209
出版年度：2011
卷号：2011
DOI：10.1155/2011/420369
出版社：Hindawi Publishing Corporation
摘要：In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM), then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC.