On the relative importance of vocal source, system, and prosody in human depression

Horwitz, Rachelle; Quatieri, Thomas F.; Helfer, Brian S.; Yu, Bea; Williamson, James R.; Mundt, James C.

doi:10.1109/bsn.2013.6575522

Cited by 52 publications

(43 citation statements)

References 16 publications

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“…In previous work, we have found that vocal biomarkers for depression assessment are most reliable when comparing identical read passages [19]. We decided therefore to focus on the third read passage, which has sufficient duration to provide robust feature estimates (mean duration = 226 seconds, with standard deviation = 66 seconds), and which is also in the speakers' common native language (German).…”

Section: Data Segmentationmentioning

confidence: 99%

Vocal biomarkers of depression based on motor incoordination

Williamson

Quatieri

Helfer

et al. 2013

Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge

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148

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1In Major Depressive Disorder (MDD), neurophysiologic changes can alter motor control [1,2] and therefore alter speech production by influencing the characteristics of the vocal source, tract, and prosodics. Clinically, many of these characteristics are associated with psychomotor retardation, where a patient shows sluggishness and motor disorder in vocal articulation, affecting coordination across multiple aspects of production [3,4]. In this paper, we exploit such effects by selecting features that reflect changes in coordination of vocal tract motion associated with MDD. Specifically, we investigate changes in correlation that occur at different time scales across formant frequencies and also across channels of the delta-mel-cepstrum. Both feature domains provide measures of coordination in vocal tract articulation while reducing effects of a slowly-varying linear channel, which can be introduced by time-varying microphone placements. With these two complementary feature sets, using the AVEC 2013 depression dataset, we design a novel Gaussian mixture model (GMM)-based multivariate regression scheme, referred to as Gaussian Staircase Regression, that provides a root-mean-squared-error (RMSE) of 7.42 and a mean-absolute-error (MAE) of 5.75 on the standard Beck depression rating scale. We are currently exploring coordination measures of other aspects of speech production, derived from both audio and video signals.

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Section: Data Segmentationmentioning

confidence: 99%

Vocal biomarkers of depression based on motor incoordination

Williamson

Quatieri

Helfer

et al. 2013

Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge

Self Cite

148

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“…Each category of features can be tested on its own or in a combined approach. Prosodic and spectral features have been found to vary in patients suffering from depression with respect to healthy subjects [4][5][6][7][8][9][10]. Prosodic, glottal, cepstral, spectral and Teager Energy Operator (TEO) related features were used in detecting depression in adolescents [8] by analysing speeches during family interaction.…”

Section: Introductionmentioning

confidence: 99%

Automatic analysis of speech F0 contour for the characterization of mood changes in bipolar patients

Guidi

Vanello

Bertschy

et al. 2015

Biomedical Signal Processing and Control

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da inserireBipolar disorders are characterized by a mood swing, ranging from mania to depression. A system that could monitor and eventually predict these changes would be useful to improve therapy and avoid dangerous events. Speech might convey relevant information about subjects' mood and there is a growing interest to study its changes in presence of mood disorders. In this work we present an automatic method to characterize fundamental frequency (F0) dynamics in voiced part of syllables. The method performs a segmentation of voiced sounds from running speech samples and estimates two categories of features. The first category is borrowed from Taylor's Tilt intonational model. However, the meaning of the proposed features is different from the meaning of Taylor's ones since the former are estimated from all voiced segments without performing any analysis of intonation. A second category of features takes into account the speed of change of F0. In this work, the proposed features are first estimated from an emotional speech database. Then, an analysis on speech samples acquired from eleven psychiatric patients experiencing different mood states, and eighteen healthy control subjects is introduced. Subjects had to perform a text reading task and a picture commenting task. The results of the analysis on the emotional speech database indicate that the proposed features can discriminate between high and low arousal emotions. This was verified both at single subject and group level. An intra-subject analysis was performed on bipolar patients and it highlighted significant changes of the features with different mood states, although this was not observed for all the subjects. The directions of the changes estimated for different patients experiencing the same mood swing, were not coherent and were task-dependent. Interestingly, a single-subject analysis performed on healthy controls and on bipolar patients recorded twice with the same mood label, resulted in a very small number of significant differences. In particular a very good specificity was highlighted for the Taylor-inspired features and for a subset of the second category of features, thus strengthening the significance of the results obtained with patients. Even if the number of enrolled patients is small, this work suggests that the proposed features might give a relevant contribution to the demanding research field of speech-based mood classifiers. Moreover, the results here presented indicate that a model of speech changes in bipolar patients might be subject-specific and that a richer characterization of subject status could be necessary to explain the observed variability

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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].…”

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

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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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On the relative importance of vocal source, system, and prosody in human depression

Cited by 52 publications

References 16 publications

Vocal biomarkers of depression based on motor incoordination

Vocal biomarkers of depression based on motor incoordination

Automatic analysis of speech F0 contour for the characterization of mood changes in bipolar patients

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

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