An Overview of Speaker Identification: Accuracy and Robustness Issues

TLDR: The main paradigms for speaker identification, and recent work on missing data methods to increase robustness are presented, and combined approaches involving bottom-up estimation and top-down processing are reviewed.

Abstract: This paper presents the main paradigms for speaker identification, and recent work on missing data methods to increase robustness. The feature extraction, speaker modeling and system classification are discussed. Evaluations of speaker identification performance subject to environmental noise are presented. While performance is impressive in clean speech conditions, there is rapid degradation with mismatched additive noise. Missing data methods can compensate against arbitrary disturbances and remove environmental mismatches. An overview of missing data methods is provided and applications to robust speaker identification summarized. Finally combined approaches involving bottom-up estimation and top-down processing are reviewed, and their significance discussed.