## 5 Improved DSR Using Phonological Features in Vocabulary-
### Results
#### 5.4.2 Very-Low-Resource Setting  
To evaluate the **PFs** method in the very-low-resource setting, we used selected training data from **D_indm**. Table 7 presents the averaged PER results across all dysarthric databases, comparing different recognition methods, data selection methods, and amounts of training data. In the table, **PHN** and **PFs** denote the utilization of the respective layer alone, whereas **Combi. ** represents using a combination of both layers. Across all data selections and training data amounts, the **PFs** method consistently outperforms the **PHN** method, highlighting its effectiveness in capturing word- and phone-independent information and its ability to generalize to unseen words and phones in very-low-resource scenarios. However, it is noteworthy that as training data increase, the advantage of the **PFs** layer diminishes. With 90% training data, the improvement in PER brought by the **PFs** layer becomes statistically insignificant. This trend may stem from the fact that when sufficient training data are available, the unique strengths of **PFs** in recognizing unseen phones or overcoming vocabulary over-fitting vanish as vocabulary mismatch is resolved. The reduced impact of the **PFs** layer is also evident with the **Random** selection method, where the training data better covers the vocabulary. Additionally, with sufficient training data, the limitations of PF-based recognition show up. It requires precise detection of each phonological feature for accurate phone recognition, which could be challenging, particularly for dysarthric speakers. Alternatively, while the PF detection may still be accurate, the Signature matrix \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**EXP**} & \multirow{2}{*}{**Data Selection**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Training Data Amount**} \\ \cline{3-5}  & & & **10\%** & **30\%** & **90\%** \\ \hline
**E9** & & **PHN** & 0.326 \(\dagger\) & 0.247 & 0.205 \\
**E21** & **Random** & **PFs** & 0.269 \(\ast\) & 0.225 & 0.196 \\
**E22** & & **Combi.** & **0.263 \(\ast\)\(\ddagger\)** & **0.216 \(\ast\)\(\ddagger\)** & **0.184 \(\ast\)\(\ddagger\)** \\ \hline
**E16** & & **PHN** & 0.690 \(\dagger\) & 0.478 \(\dagger\) & 0.258 \\
**E23** & **Ordered** & **PFs** & **0.445 \(\ast\)** & **0.355 \(\ast\)** & 0.248 \\
**E24** & & **Combi.** & 0.459 \(\ast\)\(\ddagger\)** & **0.352 \(\ast\)** & **0.240 \(\ast\)\(\ddagger\)** \\ \hline \hline \end{tabular}
**Bold numbers: Best PER(s) within statistical significance level in each case; \(\ast\): \(p\)-value \(\leq\) 0.05 compared to corresponding PHN** method; \(\ddagger\): \(p\)-value \(\leq\) 0.05 compared to corresponding **PFs** method.  
\end{table} 
Table 7: Averaged PER across all dysarthric databases, using different recognition methods, data selection methods, and amounts. may not accurately model phone realization for dysarthric speech. Due to the lack of PF ground truth, it is difficult to discriminate between these hypotheses. When the **PFs** layer is combined with the **PHN** layer for speech recognition, the combined layer performs better than using only **PFs** layer in most cases. Especially when sufficient training data are available, the performance advantage proves the supplementary effect between the two recognition layers. However, the **Combi. ** layer gives higher PER results or non-significantly lower PER results with limited training vocabulary (**Ordered** selection and 10%/30% data). In this scenario, the advantage of the **PFs** layer may be negatively influenced by the **PHN** layer, which is more sensitive to the mismatch between the training and test vocabulary. Moreover, compared to the results **E11/17** using the **PHN** layer and the vocabulary-informed base model in Table 5, the **Combi. ** layer achieves similar recognition performance without needing a suitable vocabulary-informed base model.