# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments
### Further Study  
#### 4.4.1 Single-Domain Adaptation  
Previous experiments have successfully validated the superior performance of C-LDA in CNC1.eval, which involves multiple genres so can be regarded as a multi-domain adaptation test. To further assess its potential, we chose three genres (interview, entertainment, and singing) to test its performance, where each genre is treated as a particular domain. The three genres were chosen because there is abundant data in these genres (ref. Table 1), making them suitable for evaluating the performance of C-LDA in single-domain adaptation. To perform the evaluation, we need to construct the evaluation trials as they are not involved in the standard CNC1.eval set. For each speaker of each genre, we concatenated the utterances to create a 20 s enrollment segment, while the remaining utterances were kept for testing. By cross-pairing these enrollment and test utterances, we conducted single-domain evaluation. All the unsupervised methods were considered, and the experimental results are shown in Table 5. It is important to note that for every single genre, we independently adapt the source-domain model (SSL-DA and C-FT), the back-end model (A-PLDA, CORAL+, C-PLDA) or the embeddings (C-LDA). Firstly, C-LDA excels in both multi-domain and single-domain scenarios due to the limited data for each single domain compared to the multi-domain scenario. The limited data provide a relative advantage for simple adaptation methods such as LDA, as complex front-end adaptation may suffer from over-fitting. This explains why C-LDA outperforms SSL-DA and C-FT. Additionally, the limited data, especially the limited number of speakers, may prevent an accurate estimation of between-speaker covariance. This does not impact C-LDA, as the between-class covariance does not affect its performance when scoring is based on cosine distance. However, for PLDA, the between-class covariance is essential and involved in the score computation, explaining why C-LDA outperforms A-PLDA, CORAL+, and C-PLDA in most cases. Secondly, in single-domain scenarios, SSL-DA outperforms C-FT, although it is weak in the multi-domain test. This could be attributed to the fact that there is no need to perform domain alignment for SSL-DA in the single-domain test, and contrastive loss seems mild and causes little over-fitting when used to adapt the front-end neural net. In contrast, C-FT seems to severely over-fit the training data, especially the errors in the pseudo labels. Thirdly, SSL-DA outperforms all other methods in the singing domain, whereas this is not the case in other domains. This could be because the singing domain differs significantly from the source domain (mostly interview speech). This indicates that if there \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scoring**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Domain**} \\ \cline{3-6}  & & **CNCL.dev** & **CNC1.dev.** & **CNC1.dev.** & **CNC1.dev.** \\  & & **Interview** & **Entertainment** & **Singing** \\ \hline \multirow{3}{*}{Cosine} & Pretrain & 14.22 & c9.35 & 10.88 & 28.08 \\  & SSL-DA & 11.54 & 7.02 & 7.89 & 16.93 \\  & C-FT & 11.08 & 7.62 & 8.99 & 21.54 \\  & C-LDA & 10.66 & 6.21 & 7.12 & 18.57 \\ \hline \multirow{3}{*}{PLDA} & A-PLDA & 10.86 & 6.53 & 7.29 & 18.89 \\  & CORAL+ & 10.54 & 6.72 & 7.36 & 19.23 \\ \cline{1-1}  & C-PLDA & 10.11 & 5.35 & 7.18 & 18.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: EER performance on multi/single-domain adaptation with all considered unsupervised methods. Note that the column CNC1.dev shows the multi-domain adaptation. is a significant domain shift, adapting the front-end network is still necessary, making SSL-DA better than the embedding adaptation (C-LDA) and back-end adaptation (A-PLDA, CORAL+, C-PLDA) methods. Additionally, preventing over-fitting is important, which is why SSL-DA outperformed C-FT.