Evaluation of Depression Severity in Speech

Liu, Zhenyu; Hu, Bin; Liu, Fei; Kang, Huanyu; Li, Xiaoyu; Yan, Lihua; Wang, Tianyang

doi:10.1007/978-3-319-47103-7_31

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The voice of depressed individuals reflects the perception of qualities such as monotony, slur, and less fluctuation [12]. Some researchers have supported the feasibility and validity of depression detection in speech signals [13,14], and many studies have focused on the correlation between depression and speech parameters [15,16]. Darby et al found that listeners could perceive a change in the pitch, loudness, and speaking rate of depressed patients [17].…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al employed the methods of reading, interview, and picture description to establish a Chinese language database for depression recognition [30]. They tested the performance of three widely used classifiers (SVM, naïve Bayes, and random forest) on a large voice feature data set and further testified to the effectiveness of evaluating depression severity using speech [13]. Different speech types and emotions may elicit different levels of cognitive effort and various emotional effects, producing changes in speech acoustics that affect the depression recognition [31].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram

Long¹,

Wu²,

Guo³

et al. 2017

J Health Med Inform

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Depression is a severe mental health disorder with high societal costs. Despite its high prevalence, its diagnostic rate is very low. To assist clinicians to better diagnose depression, researchers in recent years have been looking at the problem of automatic detection of depression from speech signals. In this study, a novel multi-classifier system for depression detection in speech was developed and tested. We collected speech data in different ways, and we examined the discriminative power of different speech types (such as reading, interview, picture description, and video description). Considering that different speech types may elicit different levels of cognitive effort and provide complementary information for the classification of depression, we can utilize various speech data sets to gain a better result for depression recognition. All individual learners formed a pool of classifiers, and some individual learners with a high diversity and accuracy in the pool were selected. In the process, the kappa-error diagram helped us make decisions. Finally, a multi-classifier system with a parallel topology was built, and each individual learner in this system used different speech data types and speech features. In our experiment, a sample of 74 subjects (37 depressed patients and 37 healthy controls) was tested and a leave-one-out cross-validation scheme was used. The experiment result showed that this new approach had a higher accuracy (89.19%) than that of single classifier methods (the best is 72.97%). In addition, we also found that the overall recognition rate using interview speech was higher than those employing picture description, video description, and reading speech. Furthermore, neutral speech showed better performance than positive and negative speech.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram

Long¹,

Wu²,

Guo³

et al. 2017

J Health Med Inform

Self Cite

View full text Add to dashboard Cite

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Analysis of phonetic markedness and gestural effort measures for acoustic speech-based depression classification

Stasak

Epps

Lawson

2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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While acoustic-based links between clinical depression and abnormal speech have been established, there is still however little knowledge regarding what kinds of phonological content is most impacted. Moreover, for automatic speech-based depression classification and depression assessment elicitation protocols, even less is understood as to what phonemes or phoneme transitions provide the best analysis. In this paper we analyze articulatory measures to gain further insight into how articulation is affected by depression. In our investigative experiments, by partitioning acoustic speech data based on lower to high densities of specific phonetic markedness and gestural effort, we demonstrate improvements in depressed/non-depressed classification accuracy and F1 scores.

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Speechformer-Ctc: Sequential Modeling of Depression Detection with Speech Temporal Classification

Wang,

Ravi,

Flint

et al. 2024

Preprint

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Evaluation of Depression Severity in Speech

Cited by 3 publications

References 23 publications

Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram

Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram

Analysis of phonetic markedness and gestural effort measures for acoustic speech-based depression classification

Speechformer-Ctc: Sequential Modeling of Depression Detection with Speech Temporal Classification

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