1 research outputs found
Prediction of sleepiness ratings from voice by man and machine
This paper looks in more detail at the Interspeech 2019
computational paralinguistics challenge on the prediction of
sleepiness ratings from speech. In this challenge, teams were
asked to train a regression model to predict sleepiness from
samples of the Düsseldorf Sleepy Language Corpus (DSLC).
This challenge was notable because the performance of all
entrants was uniformly poor, with even the winning system
only achieving a correlation of r=0.37. We look at whether the
task itself is achievable, and whether the corpus is suited to
training a machine learning system for the task. We perform a
listening experiment using samples from the corpus and show
that a group of human listeners can achieve a correlation of
r=0.7 on this task, although this is mainly by classifying the
recordings into one of three sleepiness groups. We show that
the corpus, because of its construction, confounds variation
with sleepiness and variation with speaker identity, and this
was the reason that machine learning systems failed to
perform well. We conclude that sleepiness rating prediction
from voice is not an impossible task, but that good
performance requires more information about sleepy speech
and its variability across listeners than is available in the
DSLC corpu