FPGA Implementation for GMM-Based Speaker Identification

Abstract EN

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.