Performance analysis of classical MAP adaptation in GMM-based speaker identification systems

Abstract

Gaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker identification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. Voice activity detection (VAD) component has been found to have a significant impact on the performance. Therefore, VAD algorithms that are robust to background noise have been proposed. Significant differences in performance have been observed between male and female speakers and GSM/PSTN channels. Moreover, single-stream GMM approach has been found to perform significantly better than the multi-stream GMM approach. It has been observed under all conditions that data duration is critical for good performance.