Depression Detection Using Automatic Transcriptions of De-Identified Speech

López-Otero, Paula; Docío-Fernández, Laura; Abad, Alberto; Garcı́a-Mateo, Carmen

doi:10.21437/interspeech.2017-1201

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…The use of word vectors for detection of pathologies and para-linguistic information in speech is very novel. [34] used the 'GloVe' word vectors to detect depression from the transcripts produced by the ASR on the de-identified speech (modifying voice characteristics for privacy reasons). They split each turn of a speaker into a series of words.…”

Section: Word Vectorsmentioning

confidence: 99%

Detecting Signs of Dementia Using Word Vector Representations

et al. 2018

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Recent approaches to word vector representations, e.g., 'w2vec' and 'GloVe', have been shown to be powerful methods for capturing the semantics and syntax of words in a text. The approaches model the co-occurrences of words and recent successful applications on written text have shown how the vector representations and their interrelations represent the meaning or sentiment in the text. Most applications have targeted written language, however, in this paper, we investigate how these models port to the spoken language domain where the text is the result of (erroneous) automatic speech transcription. In particular, we are interested in the task of detecting signs of dementia in a person's spoken language. This is motivated by the fact that early signs of dementia are known to affect a person's ability to express meaning articulately for example when they engage in a conversation -something which is known to be cognitively very demanding. We analyse conversations designed to probe people's short and long-term memory and propose three different methods for how word vectors may be used in a classification setup. We show that it is possible to identify dementia from the output of a speech recogniser despite a high occurrence of recognition errors.

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Section: Word Vectorsmentioning

confidence: 99%

Detecting Signs of Dementia Using Word Vector Representations

et al. 2018

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“…The second group includes recordings of spontaneous statements. These enable linguistic analysis, which is especially useful when diagnosing mental illness (Stasak et al, 2017;Lopez-Otero et al, 2017). The third group includes recordings from dialogues between subjects and interviewers (Weiner et al, 2016).…”

Section: Standardization Of Database Descriptionsmentioning

confidence: 99%

Speech Analysis as a Tool for Detection and Monitoring of Medical Conditions: A review

2023

Archives of Acoustics

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The goal of this article is to present and compare recent approaches which use speech and voice analysis as biomarkers for screening tests and monitoring of some diseases. The article takes into account metabolic, respiratory, cardiovascular, endocrine, and nervous system disorders. A selection of articles was performed to identify studies that assess voice features quantitatively in selected disorders by acoustic and linguistic voice analysis. Information was extracted from each paper in order to compare various aspects of datasets, speech parameters, methods of applied analysis and obtained results. 110 research papers were reviewed and 47 databases were summarized. Speech analysis is a promising method for early diagnosis of certain disorders. Advanced computer voice analysis with machine learning algorithms combined with the widespread availability of smartphones allows diagnostic analysis to be conducted during the patient's visit to the doctor or at the patient's home during a telephone conversation. Speech analysis is a simple, low-cost, non-invasive and easy-toprovide method of medical diagnosis. These are remarkable advantages, but there are also disadvantages. The effectiveness of disease diagnoses varies from 65% up to 99%. For that reason it should be treated as a medical screening test and should be an indication of the need for classic medical tests.

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“…To implement a system able to run on users' mobile terminals, some authors propose the usage of typing pattern analysis [47]. Other speech-based analysis methods, which involve the automatic transcription of speech using cloud-based resources, present a possible loss of privacy [48].…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

et al. 2021

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Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.

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Depression Detection Using Automatic Transcriptions of De-Identified Speech

Cited by 16 publications

References 18 publications

Detecting Signs of Dementia Using Word Vector Representations

Detecting Signs of Dementia Using Word Vector Representations

Speech Analysis as a Tool for Detection and Monitoring of Medical Conditions: A review

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

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