DEPAC : a Corpus for Depression and Anxiety Detection from Speech (2022)

Abstract: Mental distress like depression and anxiety contribute to the largest proportion of the global burden of diseases. Automated diagnosis system of such disorders, empowered by recent innovations in Artificial Intelligence, can pave the way to reduce the sufferings of the affected individuals. Development of such systems requires information-rich and balanced corpora. In this work, we introduce a novel mental distress analysis audio dataset DEPAC, labelled based on established thresholds on depression and anxiety standard screening tools. This large dataset comprises multiple speech tasks per individual, as well as relevant demographic information. Alongside, we present a feature set consisting of hand-curated acoustic and linguistic features, which were found effective in identifying signs of mental illnesses in human speech. Finally, we justify the quality and effectiveness of our proposed audio corpus and feature set in predicting depression severity by comparing the performance of baseline machine learning models built on this dataset with baseline models trained on other well-known depression corpora.

## introduction effective treatment for psychiatric diseases requires characterizing disease profiles with high accuracy. the traditional schema for diagnosis is based on clustering of non-specific physical and behavioral symptoms, which makes the diagnostic process challenging. for example, in major depressive disorder (mdd), high disease heterogeneity and lack of agreed-upon assessment standards necessitate a high degree of clinical experience and training to make an accurate diagnosis. both clinicianadministered and self-rated clinical assessments for mdd, such as the hamilton depression scale (ham-d) @xcite , montgomery asberg depression scale (madrs) @xcite , beck depression inventory (bdi) @xcite , and patient health questionnaire (phq-9) @xcite are suboptimal in many ways. each assess the illness through different symptom domains, have low construct validity, lack specific behavioral references, and are subjective @xcite @xcite . moreover, participants are often reluctant to fill-out the self rated assessment in regular intervals. these issues can lead to misdiagnosis, which impacts treatment timelines and can lead to poor clinical outcomes. in contrast, language can be an effective alternative to objectively characterize psychiatric illness. for example, emotion and cognition are both affected in mdd. as a result, depressed patients demonstrate negative emotional bias in memory, attention, and event-interpretation @xcite , as well as more general impairment in attention, memory, and decision-making @xcite . these effects are manifested in patients' language in a variety of ways, for example, slowed rate of speech, volume, prosody, as well as increased use of firstperson pronouns, negatively valenced speech content, and use of absolute words @xcite . therefore, automated computational analysis of speech represents an excellent data source to develop digital biomarkers for mental illness. this kind of automated assessment takes only a few minutes of audio recording, therefore is less time-consuming, and would reduce burden on the individuals. however, such model development requires access to datasets of sufficient quality and size. the recent development of speech-based computational models for measuring depression prevalence and severity has been accelerated by the introduction of audio-visual emotion recognition challenge (avec) in 2013. a subset of the audiovisual depressive language corpus (avid-corpus) was introduced as challenge corpus for 2013 @xcite and 2014 @xcite depression recognition sub-challenge (dsc) of the event. this dataset comprises 150 recordings in german language, divided equally into training, development and test partitions. predicting depression severity on bdi-ii scale was the challenge specified task. another popular dataset in this area is the distress analysis interview corpus (daic) @xcite . it contains semi-structured clinical interviews in english language formulated to support diagnosis of psychological conditions such as anxiety, depression, and post-traumatic stress disorder. different subsets of this dataset were used as the challenge corpus of avec 2016, 2017 and 2019 @xcite @xcite where challenge participants reported phq-8 scores predicted by their respective regression models. however, the depression corpora used in previous research suffer from two vital limitations. firstly, the small sample size in the existing depression datasets increases the risk of overfitting in the machine learning models. for example, the number of recordings in the avec challenges available for model training range from 50 to 189, which is far from sufficient. secondly, the datasets in the previous works lack in linguistic variety, as they only contain a small number (only one or two) of samples per subject. to mitigate these challenges, in this work we introduce the depression and anxiety crowdsourced corpus (depac) as a novel dataset that is rich in the diversity of speech tasks and subjects and is tailored to capture the signs of anxiety and depression to make accurate prediction on subjects' psychological state. we also present a set of acoustic and linguistic features extracted from the corpus which incorporates domain knowledge of clinical and machine learning experts. finally, we benchmark our dataset with several baseline machine learning models that use this set of features, to show that this novel dataset is well-suited for the machine learning-based methods with the goal of generating speech biomarkers for depression. ## depac corpus the depac corpus introduced in this work was collected with the goal of gathering a large training dataset to identify candidate speech and language features that are specific to a given psychiatric dis-ease. data collection for the corpus was approved by the institutional review board (irb). this is a proprietary dataset, collected via crowdsourcing and consists of a variety of self-administered speech tasks. the participants completed these tasks using amazon mechanical turk foot_0 (mturk), a platform where individuals are paid to complete short tasks online @xcite . the speech samples were then manually transcribed and compiled along with participant demographic information into the final corpus. ## feature sets in this section, we introduce a set of hand-crafted features extracted from the depac audio records and the associated transcripts. the set of features comprises various linguistic and acoustic features that have been found in previous psychiatric literature to be effective in detection of depression and anxiety from spoken language @xcite . ## intended usage the study aimed to collect a high quality training dataset with the intention of developing a speechbased digital biomarker for the psychiatric diseases of depression and anxiety. the dataset is wellsuited for exploratory analysis involving statistical and machine learning methods to generate potential speech biomarkers and test their validity. in section 5, we present the baseline models to predict ## conclusion in this work, we introduce depac, a rich audio dataset for mental health research which is labelled with scores on standard scales of two highly prevalent mental disorders: phq-9 scores for depression and gad-7 scores for anxiety assessment. the dataset offers a remarkably larger sample size in comparison to other publicly available corpora. one other source of novelty of the presented corpus is its richness in the diversity of speech tasks and participants with various degrees of education, genders, and age groups. we also introduce a handcurated set of acoustic and linguistic features incorporating domain knowledge of clinical and ml experts, which are used as the predictors of models for quantifying depression severity. we present the performance of baseline models in prediction of depression severity level, that can be applied by future researchers as a benchmark. our baseline models achieve competitive performance when compared to the avec 2016 and avec 2019 baseline models and demonstrate the quality of the depac dataset and effectiveness of our proposed feature set in measuring depression severity.