Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82-86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity.

/pdf/automatic-detection-of-covid-19-based-on-short-duration-187johs88h.pdf

Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis.

http://www.cell.com/article/S2666389922002276/pdf

Modern views of machine learning for precision psychiatry

Depression is a serious and common psychological disorder that requires early diagnosis and treatment. In severe episodes the condition may result in suicidal thoughts. Recently, the need for building an effective audio-based Automatic Depression Detection (ADD) system has sparked the interest of the research community. To date, most of the reported approaches to recognize depression rely on hand-crafted feature extraction for audio data representation. They combine wide variety of audio-related features to improve the classification performance. However, combining many hand-crafted features including relevant and less-relevant can enlarge the feature space which can lead to high-dimensionality issues as not all the features would carry significant information regarding depression. Having high number of features can make the pattern recognition more difficult and increase the risk of overfitting. To overcome these limitations, an audio-based framework of depression detection which includes an adaptation of a deep learning (DL) technique is proposed to automatically extract the highly relevant and compact feature set. This proposed framework uses an end-to-end Convolutional Neural Network-based Autoencoder (CNN AE) technique to learn the highly relevant and discriminative features from raw sequential audio data, and hence to detect depressed people more accurately. In addition, to address the sample imbalance problem we use a cluster-based sampling technique which highly reduces the risk of bias towards the major class (non-depressed). To evaluate the performance and effectiveness of the proposed pipeline, we perform the experiments on Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset and compare them with the hand-crafted feature extraction methods and other outstanding studies in this domain. The results show that proposed method outperforms other well-known audio-based ADD models with at least 7% improvement in F-measure for classifying depression. 

Audio based depression detection using Convolutional Autoencoder

Depression is a serious and common psychological disorder that requires early diagnosis and treatment. In severe episodes the condition may result in suicidal thoughts. Recently, the need for building an effective audio-based Automatic Depression Detection (ADD) system has sparked the interest of the research community. To date, most of the reported approaches to recognize depression rely on hand-crafted feature extraction for audio data representation. They combine wide variety of audio-related features to improve the classification performance. However, combining many hand-crafted features including relevant and less-relevant can enlarge the feature space which can lead to high-dimensionality issues as not all the features would carry significant information regarding depression. Having high number of features can make the pattern recognition more difficult and increase the risk of overfitting. To overcome these limitations, an audio-based framework of depression detection which includes an adaptation of a deep learning (DL) technique is proposed to automatically extract the highly relevant and compact feature set. This proposed framework uses an end-to-end Convolutional Neural Network-based Autoencoder (CNN AE) technique to learn the highly relevant and discriminative features from raw sequential audio data, and hence to detect depressed people more accurately. In addition, to address the sample imbalance problem we use a cluster-based sampling technique which highly reduces the risk of bias towards the major class (non-depressed). To evaluate the performance and effectiveness of the proposed pipeline, we perform the experiments on Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset and compare them with the hand-crafted feature extraction methods and other outstanding studies in this domain. The results show that proposed method outperforms other well-known audio-based ADD models with at least 7% improvement in F-measure for classifying depression.

Screening for suicidal ideation in high-risk groups such as US veterans is crucial for early detection and suicide prevention Currently, screening is based on clinical interviews or self-report measures Both approaches rely on subjects to disclose their suicidal thoughts Innovative approaches are necessary to develop objective and clinically applicable assessments Speech has been investigated as an objective marker to understand various mental states including suicidal ideation In this work, we developed a machine learning and natural language processing classifier based on speech markers to screen for suicidal ideation in US veterans Veterans submitted 588 narrative audio recordings via a mobile app in a real-life setting In addition, participants completed self-report psychiatric scales and questionnaires Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal The audios were also transcribed to extract textual features for linguistic analysis We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios A combined set of 15 acoustic and linguistic features of speech were identified by the ensemble feature selection Random Forest classifier, using the selected set of features, correctly identified suicidal ideation in veterans with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80% Speech analysis of audios collected from veterans in everyday life settings using smartphones offers a promising approach for suicidal ideation detection A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans

/pdf/acoustic-and-language-analysis-of-speech-for-suicidal-1kvjpml9dh.pdf

Acoustic and language analysis of speech for suicidal ideation among US veterans.

This paper proposes a speech-based method for automatic depression classification The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio-Visual Emotion Challenge (AVEC-2016) In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier

https://www.mdpi.com/1099-4300/22/6/688/pdf

Adrián Vázquez-Romero

Papers

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks