Autoassociative neural network models for online speaker verification using source features from vowels

We demonstrate the usefulness of excitation source information for text-dependent speaker verification. The nature of vibration of vocal folds may be unique for a given speaker. This can be studied by considering vowels, since the excitation in this case is only due to glottal vibration. Linear prediction (LP) residual contains mostly source information. We propose autoassociative neural network models for capturing speaker-specific source information present in the LP residual. Speaker models are built for each vowel to study the extent of speaker information in each vowel. Using this knowledge an online speaker verification system is developed. This study demonstrates that excitation source indeed contains significant speaker information, which can be exploited for speaker recognition tasks