Abstract
Recent advances in the speaker recognition (SR) field showed remarkably accurate and outperforming algorithms. However, their performances drastically degrade when the sparse amount of data is available. Nowadays, recognising a speaker identity when only a small amount of speech data is involved for testing and training remains a key consideration since many real world applications often have access to only speech data having a limited duration. In this study, the authors present a new improved approach, based on new information detected from the speech signal, to improve the task of automatic speaker identification. In doing so, they highlight how the detection of the speaker dialect can be explored to address the research problem related to short utterance SR. Results obtained with the new regional system are presented which provide a comparison between this system and the state-of-the-art systems for speaker identification task.
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