Automatic speech recognition (ASR) systems still underperform on speech from Dutch children with developmental language disorder (DLD), largely due to the scarcity of training data. This work investigates whether cross-lingual, child-to-child voice conversion from English children with speech sound disorder can be used to generate synthetic Dutch DLD speech for ASR fine-tuning, and what effect source-target speaker similarity has on the result. We use MeanVC to convert Dutch DLD source speech into three synthetic datasets based on high-, low-, and random-similarity pairings with English target speakers. These datasets are evaluated by fine-tuning Whisper-large-v3 on each and measuring word error rate, and by scoring speech naturalness and intelligibility using the automatic MOS predictor MBNet. Fine-tuning on original Dutch DLD data substantially outperformed all synthetic-only configurations, and adding synthetic data to the original did not improve over original-only fine-tuning. High speaker similarity did not produce the best results, while low-similarity pairings performed unexpectedly well when combined with original data. Contrary to the ASR results, all synthetic datasets scored higher on naturalness and intelligibility than the original recordings, suggesting that these measures are not reliable predictors of ASR training value for DLD child speech. Cross-lingual voice conversion from English SSD child speech does not yet provide a viable route to improving ASR for Dutch children with DLD, though targeted filtering of low-quality synthetic utterances remains a promising direction for future work. ... Read more