Regularized Auto-Associative Neural Networks for Speaker Verification

Auto-Associative Neural Network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the Universal Background Model (UBM) AANN, an AANN trained on multiple held out speakers, using corresponding speaker data. When the amount of speaker data is limited, this adaptation procedure leads to overfitting. Additionally, the resultant speaker-specific parameters become noisy due to outliers in data. Thus, we propose to regularize the parameters of an AANN during speaker adaptation. A closed-form expression for updating the parameters is derived. Further, these speaker-specific AANN parameters are directly used as features in linear discriminant analysis (LDA)/probabilistic discriminant (PLDA) analysis based speaker verification system. The proposed speaker verification system outperforms the previously proposed weighted least squares (WLS) based AANN speaker verification system on NIST-08 speaker recognition evaluation (SRE). Moreover, the proposed speaker verification system obviates the need for an intermediate dimensionality reduction (or i-vector extraction) step.