Speaker Identification using Row Mean of Haar and Kekre’s Transform on Spectrograms of Different Frame Sizes

In this paper, we propose Speaker Identification using two transforms, namely Haar Transform and Kekre’s Transform. The speech signal spoken by a particular speaker is converted into a spectrogram by using 25% and 50% overlap between consecutive sample vectors. The two transforms are applied on the spectrogram. The row mean of the transformed matrix forms the feature vector, which is used in the training as well as matching phases. The results of both the transform techniques have been compared. Haar transform gives fairly good results with a maximum accuracy of 69% for both 25% as well as 50% overlap. Kekre’s Transform shows much better performance, with a maximum accuracy of 85.7% for 25% overlap and 88.5% accuracy for 50% overlap.

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