The Impact of Speaker Diarization on DNN-based Autism Severity Estimation.

This paper presents a speech-based system for autism severity estimation combined with automatic speaker diarization. Speaker diarization was performed by two different methods. The first used acoustic features, which included Mel-Frequency Cepstral Coefficients (MFCC) and pitch, and the second used x-vectors - embeddings extracted from Deep Neural Networks (DNN). The speaker diarization was trained using a Fully Connected Deep Neural Network (FCDNN) in both methods. We then trained a Convolutional Neural Network (CNN) to estimate the severity of autism based on 48 acoustic and prosodic features of speech. One hundred thirty-two young children were recorded in the Autism Diagnostic Observation Schedule (ADOS) examination room, using a distant microphone. Between the two diarization methods, the MFCC and Pitch achieved a better Diarization Error Rate (DER) of 26.91%. Using this diarization method, the severity estimation system achieved a correlation of 0.606 (Pearson) between the predicted and the actual autism severity scores (i.e., ADOS scores). Clinical Relevance- The presented system identifies children's speech segments and estimates their autism severity sc30:310ore.

Cough detection using a non-contact microphone: A nocturnal cough study.

An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001). A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05). This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.