Because more than half of the training name pairs are from Latin language clusters, the general transliteration and language models adopted the Latin name transliteration patterns.
it obtained reasonable performance (20-30% CERs) on Latin language names, such as Spanish, English and French names, but strikingly high (over 70%) CERs on oriental language names such as Chinese and Japanese names, even though the Chinese cluster has the most training data.