In this paper, we present an automatic child-directed speech detection system to be used
in the study of child language development.
Child-directed speech(CDS) is speech that is directed by caregivers towards infants. It is
not uncommon for corpora used in child language
development studies to have a combination of CDS and non-CDS. As the size of the corpora
used in these studies grow, manual annotation of
CDS becomes impractical. Our automatic CDS detector addresses this issue.
The focus of this paper is to propose and evaluate different sets of features for the detection of CDS, using several off-the-shelf classifiers. First, we look at the performance of a set of acoustic features. We continue by combining these acoustic features with several linguistic and eventually contextual features. Using the full set of features, our CDS detector was able to correctly identify CDS with an accuracy of .88 and F1 score of .87 using Naive Bayes.
Index Terms: motherese, automatic, child-directed speech, infant-directed speech, adult-directed speech, prosody, language development
Bibliographic reference. Vosoughi, Soroush / Roy, Deb (2012): "An automatic child-directed speech detector for the study of child language development", In INTERSPEECH-2012, 2478-2481.