A GMM-Based Speaker Identification System on FPGA

Speaker identification is the process of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract and these can be exploited by extracting feature vectors such as Mel frequency cepstral coefficients (MFCCs) from the speech signal. The Gaussian Mixture Model (GMM) as a well-known statistical model 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 classification hardware implementation of a text-independent GMM-based speaker identification system. A speed factor of 90 was achieved compared to software-based implementation on a standard PC.