for that, the way I've done for TatoDetect (which is meant specifically for the task of detecting the language for "one sentence a time" ) is to have a database of N-gram huge enough for a language to be nearly sure to have "them all", so that you can consider that if your text to detect contains a N-gram that your language does not have in database, you can apply a 'decrease score' for the said language.

A regularized prior would help.

I’m also really interested in trying something like this: http://www.slideshare.net/shuyo/short-text-language-detectio... (slide 6). But I’d need a lot of training data, more than UDHR.