## 1 Introduction
This investigation aims to determine the training strategy that maximizes knowledge transfer and achieves optimal performance in speaker-independent scenarios. Furthermore, we consider the challenge of a vocabulary-mismatched setting, where the model should recognize out-of-vocabulary (OOV) words that are absent from the training data. Vocabulary mismatches often arise in two situations: very-low-resource settings, where only a limited number of words are recorded, and their phones do not cover the full phone vocabulary of the language; and cross-lingual settings, where no databases exist in the target dysarthric speaker's language. Recognition tasks for target speakers under such mismatched conditions remain under-researched. To examine this issue, we design experiments simulating these scenarios and evaluate model performance. Our results indicate that models perform poorly after conventional training in vocabulary-mismatched cases, particularly when no pretrained model with a sufficiently extensive phonetic inventory is available. To address these limitations, we propose using phonological features (PFs) as intermediate representations in phone recognition tasks. Phonological features [17; 18], also known as articulatory attributes or distinctive features, are bundles of characteristics used to describe speech sounds. PFs are shared among phones and across languages, enabling the recognition of previously unseen phones when PFs are accurately detected and prior knowledge of their composition is available. Previous works have explored the benefit of PFs brought by their independence from phones in canonical speech recognition [19] (discussed further in Section 2.2). However, the potential of PFs in solving dysarthric speech recognition under vocabulary-mismatched settings has not been studied. In this work, we apply PFs to phone recognition for dysarthric speech in vocabulary-mismatched scenarios. Specifically, PFs are extracted from speech features and transformed into phones based on prior phonetic knowledge [19]. This approach ensures that even if certain phones are missing from the training data, all PF variations may be observed, allowing the PF model to be well trained for recognition tasks. Experimental results demonstrate that employing PFs in very-low-resource and cross-lingual settings significantly improves recognition performance, mitigating mismatches between training and testing vocabularies and enhancing model accuracy and robustness. In summary, this work makes the following contributions:  
1. We systematically explore optimal model training strategies that improve DSR performance in speaker-independent scenarios. Unlike previous studies, our work incorporates the most diverse range of resources, including multilingual canonical speech data and multiple dysarthric speech datasets, to maximize knowledge transfer to the target dysarthric speaker. 2. We investigate the under-explored issue of vocabulary mismatch in dysarthric speech recognition by designing experiments that evaluate model performance in very-low-resource and cross-lingual settings. Our findings highlight the limitations of conventional recognition approaches in these scenarios. 3. We propose using phonological features as intermediate representations as the solution for enhancing phone recognition for dysarthric speech in vocabulary-mismatched cases. Experimental results confirm the effectiveness of this method in mitigating vocabulary mismatches between training and test data, significantly improving recognition performance and robustness. The paper is organized as follows: In Section 2, we introduce related work on knowledge transfer in dysarthric speech-related tasks and discuss the use of phonological features in speech technology. Section 3 explores model training strategies based on transfer learning, which enhance recognition performance in the speaker-independent setting. Section 4 examines model performance in the vocabulary-mismatched setting.