Signal-to-Signal Ratio Independent Speaker Identification for Co-channel Speech Signals

In this paper, we consider speaker identification for the co-channel scenario in which speech mixture from speakers is recorded by one microphone only. The goal is to identify both of the speakers from their mixed signal. High recognition accuracies have already been reported when an accurately estimated signal-to-signal ratio (SSR) is available. In this paper, we approach the problem without estimating SSR. We show that a simple method based on fusion of adapted Gaussian mixture models and Kullback-Leibler divergence calculated between models, achieves an accuracy of 97% and 93% when the two target speakers enlisted as three and two most probable speakers, respectively. Keywords-Speaker Identification; GMM; MAP adaptation; co-channel speech;

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Rahim Saeidi, Pejman Mowlaee, Tomi Kinnunen, Zheng-Hua Tan, Mads Græsbøll Christensen, Søren Holdt Jensen, Pasi Fränti
• Keywords: Computer Vision, Gaussian Mixture Models, ICPR 2010, Mixed Signal, Probable Speakers