This paper proposes and evaluates classifiers based on Vocal Tract Length Normalization (VTLN) in a text-dependent speaker verification (SV) task with short testing utterances. This type of tasks is important in commercial applications and is not easily addressed with methods designed for long utterances such as JFA and i-Vectors. In contrast, VTLN is a speaker compensation scheme that can lead to significant improvements in speech recognition accuracy with just a few seconds of speech samples. A novel scheme to generate new classifiers is employed by incorporating the observation vector sequence compensated with VTLN. The modified sequence of feature vectors and the corresponding warping factors are used to generate classifiers whose scores are combined by a Support Vector Machine (SVM) based SV system. The proposed scheme can provide an average reduction in EER equal to 14% when compared with the baseline system based on the likelihood of observation vectors.
Vocal Tract Length Perturbation for Text-Dependent Speaker Verification With Autoregressive Prediction Coding
