Speech Communication - Special issue on speech processing in adverse conditions
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Robustness to telephone handset distortion in speaker recognition by discriminative feature design
Speech Communication - Speaker recognition and its commercial and forensic applications
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Classification Methods for Speaker Recognition
Speaker Classification I
MLP internal representation as discriminative features for improved speaker recognition
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Durations of Context-Dependent Phonemes: A New Feature in Speaker Verification
Speaker Classification II
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This chapter describes a method for enhancing the differences between speaker classes at the feature level (feature enhancement) in an automatic speaker recognition system. The original Mel-frequency cepstral coefficient (MFCC) space is projected onto a new feature space by a neural network trained on a subset of speakers which is representative for the whole target population. The new feature space better discriminates between the target classes (speakers) than the original feature space. The chapter focuses on the method for selecting a representative subset of speakers, comparing several approaches to speaker selection. The effect of feature enhancement is tested both for clean and various noisy speech types to evaluate its applicability under practical conditions. It is shown that the proposed method leads to a substantial improvement in speaker recognition performance. The method can also be applied to other automatic speaker classification tasks.