# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments
### Main Results  
#### 4.3.1 Embedding vs. Front-End  
We first compare C-LDA with several front-end adaptation methods. All the comparison methods use cosine scoring. The results tested on CNC1.eval, in terms of EER and minDCF, are reported in Table 3.  
The 'Pretrain' method represents the pre-trained model with VoxCeleb2, which serves as the source-domain model. The other methods listed, which all use cosine scoring, are domain adaptation techniques built upon the pre-trained model. Although our focus is unsupervised methods, results of supervised methods are also reported to show the upper bound of the performance of the corresponding unsupervised methods. Note that we exclude those methods that require source data, e.g., Deep CORAL [29] and MMD [39], as they are infeasible in real-life deployment systems. The methods involved in Table 3 are summarized below. 1. **Fine-tuning (FT)**: The standard front-end adaptation approach, utilizing the development data from CNC1.dev. It uses genuine speaker labels of the speech, requires the knowledge of the source-domain model, and modifies its parameters by back-propagation. 2. **Clustering fine-tuning (C-FT)**: The same as FT, except that the speaker labels of the adaptation data are pseudo labels from the AHC algorithm (the same as C-LDA). This system is mainly used to test the quality of the pseudo labels. 3. **SSL-DA**[43]: Front-end adaptation by (1) self-supervised training with CNC1.dev; (2) distribution alignment between different genres. It does not require speaker labels but needs genre labels. 4. **Clustering LDA (C-LDA)**: The proposed method of this paper. It does not require any supervision or any knowledge of the front-end model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adaptation** & **Method** & **Supervision** & **Knowledge** & **EER(\%)** & **minDCF** \\  & & **Label** & & & **(\(P_{tar}\) = 0.05)** \\ \hline \multirow{4}{*}{Front-End} & Pretrain & - & - & 14.22 & 0.5137 \\ \cline{2-6}  & FT & Speaker & NeuralNet & 9.50 & 0.3991 \\ \cline{1-1}  & C-FT & - & NeuralNet & 11.08 & 0.4820 \\ \cline{1-1}  & SSL-DA & Domain & NeuralNet & 11.54 & 0.4551 \\ \hline \multirow{2}{*}{Embedding} & C-LDA & - & - & 10.66 & 0.4122 \\ \cline{1-1}  & S-LDA & Speaker & - & 9.75 & 0.4047 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison among various adaptation methods. The pre-trained model was trained on VoxCeleb. CNC1.dev was used for adaptation and CNC1.eval was used for test. ‘FT’ denotes fine-tuning, ‘LDA’ denotes linear discriminant analysis; ‘C-’ and ‘S-’ denote ‘clustering’ and ‘supervised’, respectively. 5. **Supervised LDA (S-LDA)**: The same as C-LDA, except that it uses genuine speaker labels. S-LDA presents the upper bound for the capability of C-LDA in compensating for the distributional distortion, by eliminating label errors. Several noteworthy observations can be obtained from Table 3. For clearance, only the EER results are referred to in the discussion. * With all the adaptation methods, the results of the adapted systems are consistently better than that of the pre-trained source-domain model (14.22%). This confirms the impact of domain mismatch and underscores the necessity of domain adaptation techniques. * FT vs. C-FT (9.50% vs. 11.08%) and S-LDA vs. C-LDA (9.75% vs. 10.66%): It reveals that clustering-based unsupervised learning methods, while not reaching the performance of supervised learning with genuine speaker labels, can still effectively alleviate the domain mismatch problem and achieve performance close to that of the supervised methods. This demonstrated that simple hierarchical clustering can produce high-quality pseudo labels. * FT vs. S-LDA (9.50% vs. 9.75%): Both of which are supervised, and the difference is that they adapt the embedding network and the embedding space, respectively. It can be observed that the performance of S-LDA is quite close to that of FT.