This paper presents two methods of Automatic Speaker Recognition (ASkR). ASkR has been largely studied in the last decades, but in most cases in mono-microphone or microphone array contexts. Our systems are placed in a binaural humanoid context where the signals captured by both ears of a humanoid robot will be exploited to perform the ASkR. Both methods use Mel-Frequency Cepstral Coding (MFCC), but one performs the classification with Predictive Neural Networks (PNN) and the other performs it with Gaussian Mixture Models (GMM). Tests are made on a database simulating the functioning of the human ears. They study the influence of noise, reverberations and speaker spatial position on the recognition rate.
['Karim Youssef', 'Bastien Breteau', 'Sylvain Argentieri', 'Jean-Luc Zarader', 'ZeFeng Wang']
Approaches for Automatic Speaker Recognition in a Binaural Humanoid Context.