Speech databases for mental disorders: A systematic review

Li, Yiling; Lin, Yi; Ding, Hongwei; Li, Chunbo

doi:10.1136/gpsych-2018-100022

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…For details of studies from before 2010 and/or focusing on clinical speech, we recommend other excellent reviews. 1,[3][4][5]9 The PRISMA flow chart in Figure 2 details the search results and inclusion/exclusion selection criteria. From the initial 1812 records retrieved from the databases, 561 duplicates were automatically removed, and a further 880 were removed after manual screening of titles and abstracts based on specific inclusion/exclusion criteria, leaving 371 in-scope articles.…”

Section: Methodsmentioning

confidence: 99%

“…2 Many clinicians also consider patients' speech patterns to assess mood, cognition, and other neurological functions. 1,[3][4][5][6][7][8][9] Audio and speech also have implicit characteristics that human experts are unable to hear and quantify. 10 However, with improvements in computational power and machine learning algorithms, 11,12 data scientists have recently been able to unlock novel dimensions of audio and provide clinicians with more information to support decision-making.…”

Section: Introductionmentioning

confidence: 99%

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Can you hear me now? Clinical applications of audio recordings

Kumar

Jaquenoud

Becker

et al. 2022

Preprint

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Audio and speech have several implicit characteristics that have the potential for the identification and quantification of clinical disorders. This PRISMA-guided review is designed to provide an overview of the landscape of automated clinical audio processing to build data-driven predictive models and infer phenotypes of a variety of neuropsychiatric, cardiac, respiratory and other disorders. We detail the important components of this processing workflow, specifically data acquisition and processing, algorithms used and their customization for clinical applications, commonly used tools and software, and benchmarking and evaluation methodologies. Finally, we discuss important open challenges for the field, and potential strategies for addressing them.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can you hear me now? Clinical applications of audio recordings

Kumar

Jaquenoud

Becker

et al. 2022

Preprint

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“…Due to the aforementioned limitations, AphasiaBank, a corpus gathering hundreds of transcripts of patients with aphasia and healthy people contributed by researchers worldwide came to our consideration (Kong & Law, 2019; Li et al., 2019; MacWhinney et al., 2011). AphasiaBank collects high‐definition multimedia samples over a wide range of tasks at the discourse level to assist in the assessment and treatment of patients with aphasia.…”

Section: Introductionmentioning

confidence: 99%

Applying core lexicon analysis in patients with anomic aphasia: Based on Mandarin AphasiaBank

Jiang

Liao

Liu

2023

Intl J Lang & Comm Disor

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BackgroundPatients with anomic aphasia experience difficulties in narrative processing. General discourse measures are time consuming and require necessary skills. Core lexicon analysis has been proposed as an effort‐saving approach but has not been developed in Mandarin discourse.AimsThis exploratory study was aimed (1) to apply core lexicon analysis in Mandarin patients with anomic aphasia at the discourse level and (2) to verify the problems with core words among people with anomic aphasia.Methods & ProcedureThe core nouns and verbs were extracted from narrative language samples from 88 healthy participants. The production of core words for 12 anomic aphasia and 12 age‐ and education‐matched controls were then calculated and compared. The correlation between the percentages and the Aphasia Quotients of the revised Western Aphasia Battery was analyzed as well.Outcomes & ResultsThe core nouns and verbs were successfully extracted. Patients with anomic aphasia produced fewer core words than healthy people, and the percentages differed significantly in different tasks as well as word classes. There was no correlation between the core lexicon use and the severity of aphasia in patients with anomic aphasia.Conclusions & ImplicationsCore lexicon analysis may potentially serve as a clinician‐friendly manner of quantifying core words produced at the discourse level in Mandarin patients with anomic aphasia.WHAT THIS PAPER ADDSWhat is already known on the subject Discourse analyses in aphasia assessment and treatment have increasingly garnered attention. Core lexicon analysis based on English AphasiaBank has been reported in recent years. It is correlated with microlinguistic and macrolinguistic measures in aphasia narratives. Nevertheless, the application based on Mandarin AphasiaBank is still under development in healthy individuals and patients with anomic aphasia.What this paper adds to existing knowledge A Mandarin core lexicon set was developed for different tasks. The feasibility of core lexicon analysis to evaluate the corpus of patients with anomic aphasia was preliminarily discussed and the speech performance of patients and healthy people was then compared to provide a reference for the evaluation and treatment of clinical aphasia corpus.What are the potential or actual clinical implications of this work? The purpose of this exploratory study was to consider the potential use of core lexicon analysis to evaluate core word production in narrative discourse. Moreover, normative and aphasia data were provided for comparison to develop clinical use for Mandarin patients with anomic aphasia.

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“…According to the World Health Organization (WHO), mental disorders are categorized by a combination of maladaptive thoughts, emotions, behaviours, and interrelationships [1]. Common mental disorders include depression, post-traumatic stress disorder (PTSD), bipolar disorder, schizophrenia, eating disorders, and speech disorders such as attention-deficit hyperactivity disorder (ADHD) and autism [1]. Mental disorders are a significant public health issue that can have profound impacts on an individual's quality of life, relationships, and ability to work or participate in other activities.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, speech analysis is vital to providing more insight into the mental activities of humans, which can be learned by Artificial Intelligence (AI) for timely screening and diagnosis [1]. Studies show a dearth of data regarding mental disorder prevalence in developing countries, compared with developed countries, due to insufficient funding, backing, and facilities [11].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach to Speech Recognition for Detection of Mental Disorders

Bindas,

Onuiri

2023

CTICTR

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Mental disorders are conditions that affect a person’s cognitive functions, behavior or thinking, thereby impairing daily functions. The dearth of trained psychologists to the high number of patients living with a mental disorder pose significant challenges in the field of mental health. This study investigates the application of deep learning techniques to speech recognition for the purpose of detecting mental disorders. The main objective of this study is to effectively identify speech patterns associated with various mental disorders and thereafter develop a robust and accurate deep learning model system that can detect risk of a mental disorder in an individual based on their voice. The research methodology involved the collection of a dataset consisting of speech recordings from individuals diagnosed with depression and post-traumatic stress disorder (PTSD). The dataset acquired was carefully curated to include symptom severity levels, and linguistic variations. The results of this study demonstrate the effectiveness of deep learning approaches in speech recognition for mental disorder detection. The trained models achieved 95% and 94% accuracy rates in identifying and differentiating speech patterns associated with depression and PTSD respectively. The findings of this study have significant implications for the field of mental health. The developed deep learning system offers a promising avenue for the early detection and monitoring of mental disorders. Further research is warranted to validate and refine the developed models using larger and more diverse dataset. Additionally, the integration of multimodal data, such as combining speech analysis with psychological or text-based data, could enhance the diagnostic accuracy and reliability of the system. Keywords: Deep Learning, Mental Disorder, Post-traumatic Stress Disorder, Speech Recognition, Depression.

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Speech databases for mental disorders: A systematic review

Cited by 4 publications

References 31 publications

Can you hear me now? Clinical applications of audio recordings

Can you hear me now? Clinical applications of audio recordings

Applying core lexicon analysis in patients with anomic aphasia: Based on Mandarin AphasiaBank

A Deep Learning Approach to Speech Recognition for Detection of Mental Disorders

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