## 2 Related Works  
### Knowledge Transfer for Dysarthric Speech Processing  
In dysarthric speech technology, transferring knowledge from training data to enhance model performance on a test set or to facilitate model adaptation to target data domains is a common strategy [10; 12; 20; 21; 22], as dysarthric data resources are too scarce for training a state-of-the-art model from scratch. Therefore, in speaker-independent settings, a model pretrained on canonical speech will be fine-tuned or adapted with data that follow a distribution closer to a target speaker's. For example, feature extractors and acoustic models pretrained or fine-tuned on _canonical_ and _out-of-domain_ dysarthric speech datasets have demonstrated improved ASR accuracy for target dysarthric datasets [23]. During pretraining, base knowledge for speech recognition is acquired from canonical speech, while fine-tuning enables the model to learn dysarthric speech patterns. Similarly, ref. [12] utilizes data from _different languages_ or _diseases_ to enhance shared knowledge across diseases or languages, improving classification accuracy for dysarthric speakers. Another key data source is in-domain canonical or dysarthric speech, which shares the same speech content as the target data. Studies such as [13; 20; 24; 25] have shown that utilizing _in-domain_ canonical or dysarthric datasets, which introduce the possible content of the target speech to models in addition to knowledge of normal or dysarthric speech patterns, improves DSR performance for unseen dysarthric speakers. In [26], in-domain control and dysarthric speech data are used to create word-level prototypes for target speaker speech recognition, leading to significant improvements in speech recognition for unseen speakers. However, this work does not address the challenges posed by vocabulary-mismatched scenarios. Given the high variability among dysarthric speakers, speaker-independent settings pose significant challenges, leading to limited research in this area. To provide a more comprehensive overview, we also discuss _speaker-dependent_ cases, which have been studied more extensively. In speaker-dependent scenarios, models are typically pretrained on source domain data and then fine-tuned using data from target speakers. One commonly used source domain is _in-domain_ dysarthric speech data from source speakers [11; 27; 28], which provide an excellent initialization for recognition models, facilitating more effective adaptation to target speakers. Another frequently employed source domain is _canonical_ speech data [20; 21], which serve as a rich resource and can be used to train large-scale, up-to-date models, ultimately resulting in improved recognition performance. Combining canonical and dysarthric speech data for model pretraining has also proven to enhance recognition performance [5; 20; 29], as this approach leverages the complementary strengths of both data types. In addition to using source domains in the same language as the target speaker, researchers have explored using _multilingual_ datasets to expand data diversity and capture more general speech patterns, which improves model robustness and recognition performance for dysarthric speech in the target language. For example, refs. [10; 30] investigate knowledge transfer between English and Japanese in the Listener model of the 'Listen, Attend and Spell' model. Similarly, ref. [31] examines bilingual model training followed by language-specific adaptation, resulting in further performance gains. In another study [32], an acoustic-to-articulatory model pretrained on typical English speech is adapted to disordered English and Cantonese dysarthric speech, demonstrating the potential of incorporating knowledge beyond conventional speech recognition features.