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Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech

11 references·3 citing·52 second hopsampled

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References

2001Emotion recognition in human-computer interaction2,601 cited
2013AVEC 2013584 cited
2006Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology380 cited
2012Vocal Acoustic Biomarkers of Depression Severity and Treatment Response324 cited
2011An investigation of depressed speech detection: features and normalization188 cited
2012Psychiatric rating scales169 cited
2013Investigating voice quality as a speaker-independent indicator of depression and PTSD141 cited
2013Diagnosis of depression by behavioural signals139 cited
2012Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech67 cited
2012Using i-vector space model for emotion recognition63 cited
2011Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis62 cited

Cited by

2019Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders45 cited
2023Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement41 cited
2014Eyes Whisper Depression40 cited

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