## 6 Conclusions and Future Work  
Dysarthric speech recognition systems significantly enhance the quality of life for individuals with dysarthria. For practical use, these systems must generalize effectively to unseen speakers and handle words or phones not encountered during training, ad \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Averaged PER Across**} \\ \cline{3-6}
**EXP** & **Method** & **Datasets** & \multicolumn{4}{c}{**Intelligibility Level**} \\ \cline{3-6}  & & & **High** & **Medium** & **Low** \\ \hline
**E19** & **PHN (E19)** & 0.840 \(\ddagger\) & 0.754 \(\ddagger\) & 0.774 \(\ddagger\) & 0.968 \(\ddagger\) \\
**E25** & **PFs** & **0.801 \(\ast\)** & **0.709 \(\ast\)** & **0.733 \(\ast\)** & **0.913 \(\ast\)** \\
**E26** & **Combi.** & 0.852 \(\ast\ddagger\) & 0.769 \(\ast\ddagger\) & 0.786 \(\ast\ddagger\) & 0.981 \(\ddagger\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: PER averaged across all datasets and across speakers at each intelligibility level, using different recognition methods. dressing limitations of small-vocabulary datasets. These requirements necessitate robust performance in speaker-independent and vocabulary-mismatched scenarios. This work focuses on improving model performance under these two scenarios through optimized training strategies. For the speaker-independent case, we systematically explored training methods to maximize knowledge transfer from available data to unseen dysarthric speech. Unlike prior research relying on limited datasets, we utilized all available data, categorized as rich-resource canonical speech, in-domain canonical speech, and dysarthric speech. Experimental results showed the optimal strategy involves training the model on rich-resource canonical speech, followed by retraining on in-domain canonical speech, and finally adapting to in-domain dysarthric speech when available. For vocabulary-mismatched scenarios, an under-explored area, we examined model performance in very-low-resource and cross-lingual settings. Results revealed the limitations of conventional training approaches, especially when models lacked sufficient vocabulary knowledge during initialization. To address these challenges, we proposed using phonological features (PFs) as intermediate representations for phone recognition. Experimental findings demonstrated that PFs effectively mitigate vocabulary mismatches and overfitting while enabling recognition of unseen words and phones due to their independence from specific phones. Combining PFs with traditional methods achieved superior recognition performance in very-low-resource scenarios when adequate training data was available. Future work will focus on enhancing the use of PFs and improving the Signature matrix. Currently derived from canonical speech, the Signature matrix does not fully account for mispronounced dysarthric phones. Experiments showed limited benefits from adapting the matrix to dysarthric data during training. To address this, we aim to develop advanced adaptation methods and create personalized Signature matrices tailored to individual speakers. Another focus is advancing cross-lingual dysarthric speech recognition to support speakers of rare languages lacking adequate training data. While our experiments in cross-lingual settings highlighted challenges, including poor recognition accuracy and vocabulary overfitting, future work will focus on mitigating these issues. This includes analyzing phone-phonological feature confusion and leveraging data augmentation techniques to improve performance in these under-explored scenarios. Conceptualization, J.Q. and H.V.h. ; methodology, J.Q. ; software, J.Q. ; validation, J.Q. ; formal analysis, J.Q. ; investigation, J.Q. ; resources, H.V.h. ; data curation, J.Q. ; writing--original draft preparation, J.Q. ; writing--review and editing, J.Q. and H.V.h. ; visualization, J.Q. ; supervision, H.V.h. ; project administration, H.V.h. ; funding acquisition, H.V.h. All authors have read and agreed to the published version of the manuscript.