Speaker Recognition from Spectrogram Images

Speaker identification is used to identify the owner of the voice among many people based on the uniqueness of everyone's speech style. In this paper, we combine Convolutional Neural Network with Recurrent Neural Network using Long Short-Term Memory models for speaker recognition and implement the deep learning architecture on our dataset of spectrogram images for 77 different non-native speakers reading the same texts in Turkish. Usage of identical text reading eliminates the possible variations and diversities on spectrograms depending on vocabularies. Experiments show that the used method is very effective on recognition rate with satisfying performance and over 98% accuracy.