An Efficient Text-Independent Speaker Identification Using Feature Fusion and Transformer Model

Automatic Speaker Identification (ASI) involves the process of distinguishing an audio stream associated with numerous speakers’ utterances. Some common aspects, such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording, make the ASI task much more complicated and complex. This research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation resources. In this research, the performance of the transformer model is investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs, delta-delta MFCCs and spectral contrast, are extracted from the ELSDSR, CSTR-VCTK, and Ar-DAD, datasets. The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on all datasets. For ELSDSR, CSTR-VCTK, and Ar-DAD, the highest attained accuracies are 0.99, 0.97, and 0.99, respectively. The experimental results reveal that the proposed technique can achieve the best performance for ASI problems.