1) Continued fine-tuning of a pretrained model can be an effective method for speaker adaptation with a small amount of speaker-specific data. 2) Having more adaptation data generally leads to better performance, with error rates decreasing as the amount of adaptation data increases from 5 to around 20-25 minutes of speech. 3) With increased training time from 300 to 1000 epochs, early stopping helps reduce error rates further while significantly decreasing training time.