Text-dependent speaker identification using spectrograms based on conditional quantization

The goal of this paper is to study a new approach to text dependent speaker identification using spectrograms. This, mainly, revolves around trapping the complex patterns of variation in frequency and amplitude with time while an individual utters a given word through spectrogram segmentation. These optimally segmented spectrograms are used as a database to successfully identify the unknown individual from his/her voice. The methodology used for identifying, rely on classification of spectrograms (of speech signals), based on template matching of the conditionally quantized frequency-time domain features of the database spectrogram samples and the unknown speech sample. Performance of this novel approach on a sample collected from 40 speakers show that this methodology can be effectively used to produce a desirable success rate.