Major depressive disorder discrimination using vocal acoustic features

Taguchi, Takaya; Tachikawa, Hirokazu; Nemoto, Kiyotaka; Suzuki, Masayuki; Nagano, Toru; Tachibana, Ryuki; Nishimura, Masafumi; Arai, Tetsuaki

doi:10.1016/j.jad.2017.08.038

Cited by 94 publications

(77 citation statements)

References 24 publications

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“…Ambulatory audio recordings have been used to identify a variety of paralinguistic vocalizations, such as laughing or crying, in both adults and children (Rao, Kim, Clements, Rozga, & Messinger, ; Yatani & Truong, ). Acoustic features have also been used to identify affect and tone within speech, including identification of basic emotions (Basu, ; Rachuri et al, ), stress (Lu et al, ) and emotional arousal (Juslin & Scherer, ) as well as clinical symptoms, such as depressive (i.e., flat) tone (Moore II, Clements, Peifer, & Weisser, ; Taguchi et al, ). While promising, as above, many of these models have been developed with small samples and require additional validation to ensure their robustness.…”

Section: Advances In Sensing and Ubiquitous Computingmentioning

confidence: 99%

Automated sensing of daily activity: A new lens into development

Barbaro

2019

Developmental Psychobiology

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Rapidly maturing technologies for sensing and activity recognition can provide unprecedented access to the complex structure daily activity and interaction, promising new insight into the mechanisms by which experience shapes developmental outcomes. Motion data, autonomic activity, and “snippets” of audio and video recordings can be conveniently logged by wearable sensors (Lazer et al., 2009). Machine learning algorithms can process these signals into meaningful markers, from child and parent behavior to outcomes such as depression or teenage drinking. Theoretically motivated aspects of daily activity can be combined and synchronized to examine reciprocal effects between children’s behaviors and their environments or internal processes. Captured over longitudinal time, such data provide a new opportunity to study the processes by which individual differences emerge and stabilize. This paper introduces the reader to developments in sensing and activity recognition with implications for developmental phenomena across the lifespan, sketching a framework for leveraging mobile sensors for transactional analyses that bridge micro‐ and longitudinal‐ timescales of development. It finishes by detailing resources and best practices to facilitate the next generation of developmentalists to contribute to this emerging area.

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Section: Advances In Sensing and Ubiquitous Computingmentioning

confidence: 99%

Automated sensing of daily activity: A new lens into development

Barbaro

2019

Developmental Psychobiology

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“…Based on the fact that MFCC is a discriminative biomarker to detect depression disorder [6], this paper firstly divides the MFCCs of the speech into many segments. Then, the proposed hybrid network is used to extract the segment-level feature for each segment.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Physiological studies [4,5,6] have shown that there are some differences in speech between depressed and normal individuals. Based on these facts, many researchers [7,8,9,10] apply machine learning methods to explore the relationship between speech and Beck Depression Inventory-II (BDI-II) scores [11], which is a scale to measure the severity of depression and involves depression score ranging from 0 to 63 (0-13 no depression, 14-19 mild depression, 20-28 moderate depression and 29-63 severe depression).…”

Section: Introductionmentioning

confidence: 99%

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Automatic Depression Level Detection via ℓp-Norm Pooling

et al. 2019

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Related physiological studies have shown that Mel-frequency cepstral coefficient (MFCC) is a discriminative acoustic feature for depression detection. This fact has led to some works using MFCCs to identify individual depression degree. However, they rarely adopt neural network to capture high-level feature associated with depression detection. And the suitable feature pooling parameter for depression detection has not been optimized. For these reasons, we propose a hybrid network and p-norm pooling combined with least absolute shrinkage and selection operator (LASSO) to improve the accuracy of depression detection. Firstly, the MFCCs of the original speech are divided into many segments. Then, we extract the segment-level feature using the proposed hybrid network, which investigates the depression-related information in the spatial structure, temporal changes and discriminative representation of short-term MFCC segments. Thirdly, p-norm pooling combined with LASSO is adopted to find the optimal pooling parameter for depression detection to generate the utterance-level feature. Finally, depression level prediction is accomplished using support vector regression (SVR). Experiments are conducted on AVEC2013 and AVEC2014. The results demonstrate that our proposed method achieves better performance than the previous algorithms.

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“…The acoustic treatment has been used recently in the diagnosis of many diseases. The MFCC for the extraction of cepstral coefficients has been used in the identification of diseases in newborns by Yasmina Kheddache and Chakib Tadj [2] also Takaya Taguchi et al [3] for the major depressive disorder discrimination and for stress recognition from speech Salsabil Besbes and Zied Lachiri work with a multitaper MFCC features [4], whereas Zied Lachiri had also works on emotion recognition [5,6]. Always at the acoustic treatment we found also Nawel SOUISSI and Adnane CHERIF they work on voice disorders identification [7].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

Drissi¹,

Soumaya²,

Nsiri³

et al. 2019

ijacsa

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The aim of this study presented in this paper is to determine the choice of the appropriate wavelet analyzer with the method of extraction of MFCC coefficients for an assistance in the diagnosis of Parkinson's disease. The analysis used is based on a database of 18 healthy and 20 Parkinsonian patients. The suggested processing is based on the transformation of the speech signal by the wavelet transform through testing several sorts of wavelets, extracting Mel Frequency Cepstral Coefficients (MFCC) from the signals, and we apply the support vector machine (SVM) as classifier. The test results reveal that the best recognition rate, which is 86.84%, is obtained by the wavelets of level 2 at 3 rd scale (Daubechie, Symlet, ReverseBior or BiorSpline) combination-MFCC-SVM.

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Major depressive disorder discrimination using vocal acoustic features

Cited by 94 publications

References 24 publications

Automated sensing of daily activity: A new lens into development

Automated sensing of daily activity: A new lens into development

Automatic Depression Level Detection via ℓp-Norm Pooling

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

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