# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
## 5 Experiments
### Experimental Setup  
We extracted 400-dimensional i-vectors. For each utterance, 25-ms frames were extracted at 10-ms intervals. Preprocessing was performed for all frames in the following order: removing the direct current (DC) offset, pre-emphasis filtering with a coefficient of 0.97, and applying a Hamming window. A 60-dimensional (19 static + energy + delta + acceleration) mel-frequency cepstral coefficients (MFCCs) was extracted from each preprocessed frame. We applied utterance-level cepstral mean normalization (CMN) using a 300-frame sliding window, then voice activity detection (VAD) to remove silent frames. A gender-independent Gaussian mixture model universal background model (GMM-UBM) consists of 1024 mixture components with diagonal covariance, which was trained for 10 iterations. A gender-independent 400-dimensional i-vector extractor was trained for five iterations. Length normalization was applied to i-vectors. The common configurations for extracting deep speaker embeddings were as follows. A 40-dimensional mel-filterbank feature was extracted from each preprocessed frame. The preprocessing is the same as that used for i-vector extraction. As in the i-vector extraction, the CMN and VAD were applied to the mel-filterbank feature. Except for extracting d-vectors, the sequences of mel-filterbank features of the utterances were truncated or padded along the time axis to have lengths of 250 and 150 for parts 1 and 2, respectively. For extracting d-vectors, the sequences of mel-filterbank features were handled by using distortion-free method [58]. The speaker-and-phrase discriminative networks have two softmax classifiers: one is the speaker classifier, and the other is the phrase classifier. Unless otherwise noted, all weights of the networks were initialized from the Glorot normal distribution [59] and those of the classifiers were orthogonalized with no bias. The AMSGrad [60], a variant of the Adam [61] optimizer, was used to minimize the cross-entropy loss with a learning rate of \(10^{-3}\). We trained 100 epochs with a mini-batch size of 32 and selected the best model by validation. The extraction process for the d-vector is the same as in [58]. We extracted a 512-dimensional d-vector from a 2-layer LSTM. Each layer of the LSTM has 512 units. On top of the LSTM is a self-attention [62] layer with four attention heads, followed by a batch normalization [63] layer. The recurrent weights of the LSTM were orthogonalized. The biases of the forget gate of the LSTM were initialized to 1 [64], and the other biases were initialized to 0. The d-vector is the result of the batch normalization of the sum of the output of the attention layer. We extracted the 256-dimensional r-vector from SE-ResNet34. Except for the SE blocks, SE-ResNet34 has the same structure as ResNet34 in [37] up to the last residual block. The layers were stacked on top of the last residual block in the following order: the statistical pooling layer to compute the mean and standard deviation along the time axis for each channel, the flattening layer, the 256-dimensional fully connected layer followed by a batch normalization layer, and the classifiers. We used the batch-normalized output vector as the r-vector. We extracted the 512-dimensional x-vector. The architecture of the x-vector network is the same as the standard DNN in [29]. We applied batch normalization after each activation layer. The x-vector is the output of the layer segment 2 in [29]. The PLDA model was trained using each embedding for 10 iterations. For the GLASSO-PLDA, we first estimated the GLASSO-PLDA models with different values of \(\rho\) in the range 0 to 0.5 at intervals of 0.0005. The maximum number of iterations of the GLASSO was set to 100, and the tolerance for convergence (corresponding to the duality gap [65]) was set to \(10^{-4}\). We then computed equal error rates (EERs) for each GLASSO-PLDA model on the validation trials. The EER is the rate when the false positive rate and false negative rate are equal.