  The detection of perceived prominence in speech has attracted approaches
ranging from the design of linguistic knowledge-based acoustic features to the
automatic feature learning from suprasegmental attributes such as pitch and
intensity contours. We present here, in contrast, a system that operates
directly on segmented speech waveforms to learn features relevant to prominent
word detection for children's oral fluency assessment. The chosen CRNN
(convolutional recurrent neural network) framework, incorporating both
word-level features and sequence information, is found to benefit from the
perceptually motivated SincNet filters as the first convolutional layer. We
further explore the benefits of the linguistic association between the prosodic
events of phrase boundary and prominence with different multi-task
architectures. Matching the previously reported performance on the same dataset
of a random forest ensemble predictor trained on carefully chosen hand-crafted
acoustic features, we evaluate further the possibly complementary information
from hand-crafted acoustic and pre-trained lexical features.
