## 2 Related Works
### Phonological Features Used in Speech Technology  
In speech technology, PFs help to systematically analyze speech and contribute to speech classification and recognition within and across languages. In canonical speech recognition, PFs are commonly used in multilingual and cross-lingual settings. Studies like [33; 34; 35] have trained PF extractors demonstrating general advantages of integrating detected PFs with traditional acoustic features for phone recognition tasks across various model architectures and languages. Beyond direct integration at the feature level, other approaches have explored the transformation of detected PFs into phone tokens using prior phonetic knowledge. For instance, refs. [19, 36, 37, 38] achieved better recognition of unseen phones or in cross-lingual scenarios by leveraging such transformations on multilingual datasets. In [39], the focus was on better initializing the output layer for phone recognition. This work utilized detected PFs of unseen phones to determine their closest seen phones and initialized the weights of the unseen phones using those of the closest seen phones, significantly enhancing recognition performance for unseen phones. Additionally, ref. [40] proposed a more compact transformation between articulatory attributes and speech transcriptions, achieving significantly better recognition performance compared to conventional word- or character-based recognition models.  
In the domain of dysarthric speech, research on PFs spans various tasks. One prevalent task involves using neural networks to detect PFs from dysarthric speeches in different languages. Studies such as [8, 41, 42, 43, 44] have demonstrated that PF detectors pretrained on canonical speech generalize effectively, enabling reliable PF extraction from dysarthric speech across different language datasets and base model architectures. Accurate PF detection also enables the automatic assessment and analysis of dysarthric speakers based on their pronunciation profile. Research in this field [43, 45, 46, 47] uses PFs either directly obtained from their speech or transformed from recognized phones to provide insights into individual speech characteristics. For speech recognition tasks, detected PFs have been employed to enhance recognition performance by combining them with traditional acoustic features or model representations [8, 48, 49, 50, 51]. Additionally, PFs have been employed as a form of regularization during model training, as shown in [52], to further improve the adequacy and robustness of the information extracted during model training.  
Across all approaches explored in previous studies, PFs have consistently served an auxiliary role in achieving more accurate characteristic modeling of speech or speakers, ultimately benefiting primary tasks such as speech recognition. For dysarthric speech recognition, their effectiveness has been demonstrated primarily when used as supplementary information alongside acoustic features or model representations. The unique advantage of PFs, their independence from specific phones and languages, enables recognition of unseen phones in vocabulary-mismatched settings, a capability that has been validated in canonical speech recognition. However, this potential remains under-explored in dysarthric speech tasks. This study addresses this gap by employing PFs as intermediate representations to enhance model performance on DSR tasks in vocabulary-mismatched scenarios.