Speaker models generated from training recordings of different speakers should differentiate between speakers. These models are estimated using feature vectors that are based on acoustic observations. So, the feature vectors should themselves show a high degree of inter-speaker variability and a low degree of intra-speaker variability.


Cepstral coefficients of transformed short-time spectra (e.g. Mel-Frequency Cepstral Coefficients - MFCC) are experimentally developed features that are widely used in the domain of automatic speech and speaker detection. Because of the manifold possibilities of parameters for the feature extraction process and the lack of theoretically motivated explanations for the determination of the last-mentioned, only a stepwise investigation of the extraction process can lead to stable acoustic features.


Optimized acoustic features for the representation of speakers enables the improvement of automatic speaker identification and verification. Additionally, the development of methods for forensic investigation of speakers (manually and automatically) is supported.