## 4 Exploring Vocabulary-Mismatched Dysarthric Speech Recognition
### Results
#### 4.3.1 Very-Low-Resource Setting  
Table 5 provides the PER results of target speaker speech recognition in the very-low-resource setting. It compares results obtained with different data selection methods, amounts of training data, and model initializations. Specifically, **E9** and **E16** use the base model with a new output layer, whereas **E11** and **E17** are based on the vocabulary-informed base model **E3**.  
The results for the **Random** selection method indicate that increasing the amount of training data consistently improves PER, highlighting the importance of training data quantity. Comparing the **Ordered** and **Random** methods, we observe a consistent performance advantage for the **Random** method, but the performance gap between the two cases narrows as the amount of training data increases. This suggests the significant model performance enhancement brought by the improved vocabulary coverage in training data. Notably, when using only 10% of the training data with the **Ordered** method, the model shows a high PER of 0.690, underscoring the limitations of conventional model training approaches in very-low-resource scenarios.  
When comparing the results of different model initializations, it is evident that employing the vocabulary-informed base model **E3** provides a consistent advantage across all data selection methods and training data amounts. This advantage is weakened as the amount of training data increases, indicating that increased training data help the model to adapt better to limited vocabulary training data while exacerbating forgetting of the knowledge acquired during pretraining. Comparing results from **E9/11** and **E16/17**, the benefit of using the vocabulary-informed base model is particularly pronounced in the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**EXP**} & \multirow{2}{*}{**Data Selection**} & \multirow{2}{*}{**Training Strategy**} & \multicolumn{3}{c}{**Training Data Amount**} \\ \cline{3-6}  & & & **10\%** & **30\%** & **90\%** \\ \hline
**E9** & \multirow{2}{*}{**Random**} & **D\_indm** & 0.326 & 0.247 & 0.205 \\
**E11** & & **E3\(\rightarrow\) D\_indm** & **0.251** & **0.213** & **0.185** \\ \hline
**E16** & \multirow{2}{*}{**Ordered**} & **D\_indm** & 0.690 & 0.478 & **0.258** \\
**E17** & & **E3\(\rightarrow\) D\_indm** & **0.361** & **0.320** & **0.241** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Averaged PER results across all dysarthric datasets of models trained with different data selections, amounts of training data, and model initializations. (A \(\rightarrow\) B: train model A with data B.)  
**Ordered** method. This is likely because the extensive pretrained knowledge in **E3** serves as a crucial complement to the limited vocabulary in the **Ordered** experiment. By contrast, with the **Random** selection method, which already provides more comprehensive training vocabulary coverage, the additional benefit from **E3** is reduced. Another factor is the larger overlap between the vocabularies of the training and validation sets in the **Random** method, enabling the model to adapt more effectively to the training data and more readily forget the pretrained knowledge embedded in **E3**.