An overview of text-independent speaker recognition: From features to supervectors

This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions. Key words: Speaker recognition, text-independence, feature extraction, statistical models, discriminative models, supervectors, intersession variability compensation

• Type: Journal
• Year: 2010
• Where: SPEECH
• Authors: Tomi Kinnunen, Haizhou Li
• Keywords: Automatic Speaker Recognition, Speaker Recognition, Speaker Recognition Technology, SPEECH 2010