Text-independent distributed speaker identification and verification using GMM-UBM speaker models for mobile communications

Abstract

This paper presents the simulation results of a speaker identification and verification (SIDV) system that would be efficient for resource limited mobile devices. The proposed system works as a text-independent system within the distributed speech recognition (DSR) framework and is designed to identify a target speaker or imposter using short digit utterances rather than long utterances. In this distributed SIDV (DSIDV), the target speaker model is developed by using the most popular generative system called a GMM-UBM system. A Gaussian Mixture Model (GMM) for each true speaker is derived from the Universal Background Model (UBM) by using Bayesian maximum a posteriori (MAP) adaptation. The objective of this paper is to show how speaker recognition and verification over telephone channels can be done using short speeches and DSR technology robust to channel distortions. The ETSI Aurora2 speech corpus was tested in these experiments. The experimental results show that the proposed DSIDV system yields excellent identification and detection performances in a ETSI DSR evaluation task and would be suitable for small hand held mobile devices.