Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Speech-based degree of sleepiness estimation is an emerging research problem. In the literature, this problem has been mainly addressed through modeling of low level of descriptors. This paper investigates an end-to-end approach, where given raw waveform as input, a neural network estimates at its output the degree of sleepiness. Through an investigation on the continuous sleepiness sub-challenge of the INTERSPEECH 2019 Computational Paralinguistics Challenge, we show that the proposed approach consistently yields performance comparable or better than low level descriptor-based, bag-of-audio-words-based and sequence-to-sequence autoencoder feature representation-based regression systems. Furthermore, a confusion matrix analysis on the development set shows that, unlike the best baseline system, the performance of our approach is not centering around a few degrees of sleepiness, but is spread across all the degrees of sleepiness.