## 1 Introduction  
Dysarthria is a speech disorder caused by neuromuscular diseases or conditions, characterized by poor pronunciation. It leads to reduced intelligibility, which significantly hinders daily communication for individuals with dysarthria [1]. Automatic speech recognition (ASR) systems, which have demonstrated high effectiveness for canonical speech, hold great potential for improving communication in this population [2]. In addition, speakers with diseases that cause dysarthria often have limited motor control and need assistive devices to compensate for that. In this context, ASR can be instrumental in developing voice-controlled assistive devices, offering enhanced independence and accessibility. State-of-the-art dysarthric speech recognition (DSR) models, typically leveraging deep neural networks, have achieved satisfactory recognition performance [2, 3, 4, 5, 6, 7, 8]. An ideal DSR model, however, must go beyond high accuracy to exhibit robust generalization capabilities and perform well on speech from unseen users (target speakers) excluded from the training set. This requirement, referred to as the speaker-independent setting, is essential for reducing the need for additional data collection and model retraining, thereby minimizing user effort and enabling the model to adapt more effectively to user variability. Beyond generalization to new speakers, an ideal DSR model should also handle words not present in the training data. Current dysarthric speech databases typically contain less than ten hours of recordings and a vocabulary limited to only a few hundred words. These limited training data do not cover the diverse vocabulary needed for everyday communication. Consequently, an ASR model that recognizes only the vocabulary seen during training would have limited usability in the real world [9]. This challenge is framed as the vocabulary-mismatched setting, in which the DSR model must perform reliably even when there is a mismatch between the vocabulary used during training and testing. In this work, we focus on improving dysarthric speech recognition under two critical scenarios: speaker-independent and vocabulary-mismatched settings. Instead of developing new model architectures, our emphasis lies on identifying effective training strategies and identifying appropriate training datasets and approaches that enable the model to acquire and transfer relevant knowledge for accurate recognition of target dysarthric speaker speech. In the speaker-independent setting, the model does not have direct access to the specific speech patterns of the target speaker. However, it can still benefit from leveraging shared characteristics among dysarthric speakers, such as similarities in speech patterns that arise due to shared medical diagnoses, gender, or age. Additionally, linguistic commonalities can be exploited: for example, dysarthric and canonical speakers of the same language share overlapping vocabulary, and even across different languages, phonetic units often exhibit similarities. By training on data from other dysarthric speakers, canonical speakers of the same language, or languages with shared phonetic inventories, the model learns knowledge that can improve its recognition performance for unseen dysarthric speakers, which has been validated in previous works [10; 11; 12; 13]. However, previous research has largely focused on specific domains of source data (details are discussed in Section 2.1), leaving the integration of diverse resources unexamined. To address this gap, we systematically investigate comprehensive training strategies that integrate diverse resources, including multilingual canonical speech data, in-domain canonical speech data that align with the content of the target dysarthric speech, and dysarthric speech data within or outside the target speaker's dataset. Using the widely adopted transformer-based Wav2 Vec model [7; 14; 15; 16] as the base model, we evaluate the recognition performance of target speaker speech across various combinations of these data sources.