Emotional aspects of intrinsic speech variabilities in automatic speech recognition

We analyze two German databases: the OLLO database designed for doing speech recognition experiments on speech variabilities, and
the Berlin emotional database designed for the analysis and synthesis of emotional speech. The paper tries to find a relation between intrinsic
speech variabilities and the emotions. Moreover, we study this relation
from the point of view of speech recognition. Acoustical analysis is
performed on both databases, using Normalized Amplitude Quotient and F0
parameterization of five analyzed vowels [a], [e], [i], [o], and [u],
merging their long and short variants. Euclidean distance between the
feature vectors of both databases is used for finding the relation,
named as emotional aspect of speech variabilities. The speech
recognition experiments on the OLLO database show that found emotional
aspects have also a discrimination power.