Speaker identification under background noise using features extracted from steady vowel regions

In this paper, we are exploring features extracted from steady vowel segments for improving the performance of speaker identification system under background noise. Steady vowel regions are produced by periodic impulse-like excitation and they contain relatively high signal energy. Hence, speaker specific information present in steady vowel regions may be less affected by the noise. In this work, steady vowel regions are determined by using the knowledge of accurate vowel onset points and epochs. Speaker identification studies are carried out using TIMIT database for white and vehicle noises. Universal background model-Gaussian mixture model-based modeling is explored for developing speaker models. Significant improvement in the performance of speaker identification is observed by using features extracted from steady vowel region in presence of noisy environments. Copyright (c) 2012 John Wiley & Sons, Ltd.