  Traditional speech emotion recognition (SER) evaluations have been performed
merely on a speaker-independent condition; some of them even did not evaluate
their result on this condition. This paper highlights the importance of
splitting training and test data for SER by script, known as sentence-open or
text-independent criteria. The results show that employing sentence-open
criteria degraded the performance of SER. This finding implies the difficulties
of recognizing emotion from speech in different linguistic information embedded
in acoustic information. Surprisingly, text-independent criteria consistently
performed worse than speaker+text-independent criteria. The full order of
difficulties for splitting criteria on SER performances from the most difficult
to the easiest is text-independent, speaker+text-independent,
speaker-independent, and speaker+text-dependent. The gap between
speaker+text-independent and text-independent was smaller than other criteria,
strengthening the difficulties of recognizing emotion from speech in different
sentences.
