Influence of Feature Dimensionality and Model Complexity on Speaker Verification Performance

Abstract

This paper provides description of a text dependent speaker recognition system based on vector quantization approach. The scope of this paper is to check influence of feature dimensionality and the complexity of the speaker model on verification process. Provided results show that MFCC features yield the lowest possible verification errors among all tested parameters. Although dimensionality of feature vectors is important, there is no need to increase it above some level as the improvement in verification performance is relatively low and computational complexity increases. Far more important than dimensionality is complexity of the speaker model. © Springer International Publishing Switzerland 2014.