A study on evaluating the effect of voice activity detection (VAD) approach on speech emotion recognition of autistic children

Autism spectrum is a neurological disorder that manifests itself in the early years of a child's development. People with autism face challenges in regulating emotions and express their emotional states in different ways. The current research presents a vocal activity detection (VAD) system adapted to the voices of autistic children.

The proposed VAD system is a Recurrent Neural Network (RNN) with short-term memory (LSTM) cells. The data includes 25 English-speaking autistic children performing a structured learning activity and was collected as part of the DE-ENIGMA project.

Our experiments show that the pediatric VAD system performs less well than our generic VAD system trained under the same conditions, as we obtain system performance characteristic curve under the curve (ROC-AUC) criteria of 0.662 and 0.850, respectively. The SER results show different performances between capacity and excitation, depending on the VAD system used, with a maximum match correlation coefficient (CCC) of 0.263 and a minimum root mean square error (RMSE) of 0.107.

Although the performance of SER models is generally low, the pediatric VAD system can lead to slightly improved results compared to other VAD systems and especially the VAD-less baseline, which supports the hypothesized importance of pediatric VAD systems in the context under discussion.