Classifications of clinical depression detection using acoustic measures in Malay speakers

Azam, Huda; Hashim, Nik Nur Wahidah Nik; Sediono, Wahju; Mukhtar, Firdaus; Ibrahim, Normala; Mokhtar, Syarifah Suziah Syed; Aziz, Salina Abdul

doi:10.1109/iecbes.2016.7843521

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Based on Table 1, depression identification using speech has been studied with multiple languages and so far, for Bahasa Malaysia, only a small database has been gathered and studied. The work in [16] shows the effectiveness of MFCC on classifying the depression and healthy speech. Based on the literature that shows ADD is dependent on language and the nature of this study's database collected using multiple recordings, we cannot compare our findings with the output reported in [16] because of the dependencies of MFCC on recording devices [28].…”

Section: Comparison Of Classifiers Performance On Depressed Speech Classificationsmentioning

confidence: 99%

“…The work in [16] shows the effectiveness of MFCC on classifying the depression and healthy speech. Based on the literature that shows ADD is dependent on language and the nature of this study's database collected using multiple recordings, we cannot compare our findings with the output reported in [16] because of the dependencies of MFCC on recording devices [28]. However, for a language independent based comparison, a simple classifier such as SVM has shown to be powerful in identifying depression speech [14], [20] which is similar to our findings.…”

Section: Comparison Of Classifiers Performance On Depressed Speech Classificationsmentioning

confidence: 99%

“…Deep learning requires a large training data due to the huge number of parameters needed to be tuned by a learning algorithm and computation power for data processing. Previous studies in the field of ADD using machine learning classifiers have used binary logistic regression (LR) [9], [10], gaussian mixture model (GMM) [11]- [14], discriminant analysis [15], [16], support vector machine (SVM) [11], [17], [18], k-nearest neighbour (KNN) [17], gaussian naïve Bayes (GNB) [19] random forest (RF) [20] and decision tree [21]. The common classification method used for detecting depression in speech are SVM and GMM.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech

Hashim

Basri

Ezzi

et al. 2022

IJ-AI

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Early detection of depression allows rapid intervention and reduce the escalation of the disorder. Conventional method requires patient to seek diagnosis and treatment by visiting a trained clinician. Bio-sensors technology such as automatic depression detection using speech can be used to assist early diagnosis for detecting remotely those who are at risk. In this research, we focus on detecting depression using Bahasa Malaysia language using speech signals that are recorded remotely via subject’s personal mobile devices. Speech recordings from a total of 43 depressed subjects and 47 healthy subjects were gathered via online platform with diagnosis validation according to the Malay beck depression inventory II (Malay BDI-II), patient health questionnaire (PHQ-9) and subject’s declaration of major depressive disorder (MDD) diagnosis by a trained clinician. Classifier models were compared using time-based and spectrum-based microphone independent feature set with hyperparameter tuning. Random forest performed best for male reading speech with 73% accuracy while support vector machine performed best on both male spontaneous speech and female reading speech with 74% and 73% accuracy, respectively. Automatic depression detection on Bahasa Malaysia language has shown to be promising using machine learning and microphone independent features but larger database is necessary for further validation and improving performance.

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Section: Comparison Of Classifiers Performance On Depressed Speech Classificationsmentioning

confidence: 99%

Section: Comparison Of Classifiers Performance On Depressed Speech Classificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech

Hashim

Basri

Ezzi

et al. 2022

IJ-AI

View full text Add to dashboard Cite

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“…Resampling methods like Equal-Test-Train, Jackknife and Cross Validation were used in LDA and QDA. In future, additional acoustic features can be added to make the approach more effective [6].…”

Section: Application Of Various Machine Learning Techniques In Sentimmentioning

confidence: 99%

Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

2019

IJITEE

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Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

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“…Quranic letters can be identified using the formant frequencies, and the PSD can improve the accuracy of the system as compared to the formants [21]. On the other hand, many studies have utilized the PSD as a feature extraction technique for automatic speech recognition [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach

et al. 2022

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Reading Quran for non-Arab is a challenge due to different mother tongues. learning Quran face-to-face is considered time-consuming. The correct pronunciation of Makhraj and Sifaat are the two things that are considered difficult. In this paper, Sifaat evaluation system was developed, focusing on Sifaat with opposites for teaching the pronunciation of the Quranic letters. A classifier-based approach has been designed for evaluating the Sifaat with opposites, using machine learning technique; the k-nearest neighbour (KNN), the ensemble random undersampling (RUSBoosted), and the support vector machine (SVM). Five separated classifiers were designed to classify the Quranic letters according to group of Sifaat with opposites, where letters that are classified to the wrong groups are considered mispronounced. The paper started with identifying the acoustic features to represent each group of Sifaat. Then, the classification method was identified to be used with each group of Sifaat, where best models were selected relying on various metrics; accuracy, recall, precision, and F-score. Cross-validation scheme was then used to protect against overfitting and estimate an unbiased generalization performance. Various acoustic features and classification models were investigated, however, only the outperformed models are reported in this paper. The results showed a good performance for the five classification models.

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Classifications of clinical depression detection using acoustic measures in Malay speakers

Cited by 7 publications

References 14 publications

Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech

Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech

Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach

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