# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments
### System Configuration  
For the front-end embedding model, we adopted the x-vector architecture. The acoustic features are 80-dimensional Filter Banks (Fbanks). The primary architecture comprises three key components: (1) An ECAPA-TDNN [4] backbone, which is currently the most popular TDNN-series model for speaker verification, for learning frame-level features; (2) An attentive statistic pooling (ASP) [6] layer for aggregating frame-level features and a dense layer that projects the pooled features into an utterance-level x-vector; (3) A fully connected layer to classify different speakers in the training data. Table 2 shows the topology of this model. During the pre-training phase, the front-end embedding model was trained for 80 epochs. Each mini-batch involved 256 2-s segments randomly sampled from 5994 speakers in the VoxCeleb2 dataset. The additive angular margin softmax (AAM-Softmax) [51] was employed to discriminate speakers, with a margin value of 0.2 and a scale factor of 30. In addition, extensive data augmentation techniques were used, including SpecAugment [52], additive noise and room impulse response (RIR) simulation. Specifically, for SpecAugment, we randomly masked 0 to 10 frames in the time domain and 0 to 8 channels in the frequency domain. Additive noise was sourced from audio clips in the MUSAN corpus [53], and simulated filters of small, medium, and large rooms released in [54] were used for RIR. The model was trained using the Adam optimizer with an initial learning rate set to 0.001, which decreased by 5% each epoch. Once the learning rate dropped below 0.0001, it remained fixed. During the domain adaptation phase, for all the considered front-end adaptation methods, the mini-batch size was set to 128 and the initial learning rate was set to 0.0005 decreased by 5% at each epoch. The duration of the input segment is kept at 2 s. If it is less than 2 s, zero-padding will be applied. The entire adaptation process involved 20 epochs. The same data augmentation techniques as in the pre-training phase were used, except that SpecAugment was excluded. For C-LDA, the complete utterance is fed into the front-end \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **\# of Spks** & **\# of Utts** & **\# of Hours** & **\# of Avg Dur** \\ \hline CN-Celeb1 & 997 & 126,532 & 271.71 & 7.73 \\ \hline CNC1.dev & 797 & 107,953 & 228.01 & 7.60 \\ CNC1.eval & 200 & 18,579 & 43.71 & 8.47 \\ \hline CNC1.dev.interview & 637 & 53,035 & 120.94 & 8.21 \\ CNC1.dev.entertainment & 351 & 18,443 & 27.40 & 5.35 \\ CNC1.dev.singing & 250 & 10,544 & 23.83 & 8.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The datasets of CN-Celeb1 used in our experiment. # denotes number. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Layer** & **Kernel Size** & **Stride** & **Dilation** & **Output** \\ \hline Input & – & – & – & 80 \(\times\) 200 \\ Conv1D & \(1\times 5\) & \(1\times 1\) & \(1\times 1\) & 1024 \(\times\) 200 \\ \hline SE-Res2 Block1 & \(1\times 3\) & \(1\times 1\) & \(1\times 2\) & 1024 \(\times\) 200 \\ \hline SE-Res2 Block2 & \(1\times 3\) & \(1\times 1\) & \(1\times 3\) & 1024 \(\times\) 200 \\ \hline SE-Res2 Block3 & \(1\times 3\) & \(1\times 1\) & \(1\times 4\) & 1024 \(\times\) 200 \\ \hline SE-Res2 Block4 & \(1\times 1\) & \(1\times 1\) & \(1\times 1\) & 1536 \(\times\) 200 \\ \hline Pooling & & ASP & & 3072 \(\times\) 1 \\ \hline Dense & & – & & 192 \(\times\) 1 \\ Dense & & AAM-Softmax & & \# of Spks \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The topology of the ECAPA-TDNN model used in our experiment. # denotes number. model to obtain speaker embedding, and then agglomerative hierarchical clustering (AHC) was used to generate pseudo-speaker labels with which the full-rank LDA was trained. The number of clusters was set to 800, and the dimensionality of LDA-transformed x-vector was set to 192. Further analysis of these two parameters will be discussed in Section 4.4.2. For model evaluation, we adopted two performance metrics.