Depa

Zhang, Pingyue; Wu, Mengyue; Dinkel, Heinrich; Yu, Kai

doi:10.1145/3474085.3479236

Cited by 28 publications

(10 citation statements)

References 17 publications

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“…DNN trained on such data would lead to under-fitting; consequently, the classification result needs to be more convincing. One workable solution to the above problem is to pre-train a model on extensive data followed by leveraging the model’s knowledge to downstream tasks [e.g., speaker recognition ( Snyder et al, 2018 ), PD detection ( Moro-Velazquez et al, 2020 ), depression detection ( Zhang et al, 2021 )]. Primarily, results in Zhang et al (2021) showed that the larger out-domain (e.g., speech recognition) dataset for audio embedding pre-training generally improves performance better than the relatively little in-domain (depression detection) dataset.…”

Section: Related Workmentioning

confidence: 99%

“…One workable solution to the above problem is to pre-train a model on extensive data followed by leveraging the model’s knowledge to downstream tasks [e.g., speaker recognition ( Snyder et al, 2018 ), PD detection ( Moro-Velazquez et al, 2020 ), depression detection ( Zhang et al, 2021 )]. Primarily, results in Zhang et al (2021) showed that the larger out-domain (e.g., speech recognition) dataset for audio embedding pre-training generally improves performance better than the relatively little in-domain (depression detection) dataset. Therefore, we pre-trained speaker embedding extractors on CN-Celeb ( Fan et al, 2020 ), a large-scale Chinese speaker recognition dataset, followed by extracting corresponding embeddings on our Chinese depression speech dataset.…”

Section: Related Workmentioning

confidence: 99%

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Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

Liu

Li³

et al. 2023

Front. Neurosci.

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IntroductionAs a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects’ speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance.MethodsThe above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method.ResultsExperimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database.DiscussionOur multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

Liu

Li³

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…One is to extract hand‐crafted features from speech signals and then input them into deep neural network [37], where deep framework is only used as classifier. The other is to apply an end‐to‐end deep architecture, which feeds the original audio signal or spectrum to deep network to learn high‐level features automatically [38]. As it could solve the problems encountered in hand‐crafted features, such as high threshold, labour cost and low feature utilization rate, deep learning slowly becomes the leader in the field of machine learning.…”

Section: Research Evolutionmentioning

confidence: 99%

“…It avoids problems such as gradient disappearance to a certain extent, and can relatively learn information of long time series, so it is suitable for time series data such as speech. Since deep learning methods have been popularised in the field of SDR, a number of RNN‐based studies have been carried out [37, 38, 77, 88, 89]. Alhanai et al.…”

Section: Research Evolutionmentioning

confidence: 99%

Automatic depression recognition by intelligent speech signal processing: A systematic survey

Wang

Lin

et al. 2022

CAAI Trans on Intel Tech

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Depression has become one of the most common mental illnesses in the world. For better prediction and diagnosis, methods of automatic depression recognition based on speech signal are constantly proposed and updated, with a transition from the early traditional methods based on hand-crafted features to the application of architectures of deep learning. This paper systematically and precisely outlines the most prominent and up-to-date research of automatic depression recognition by intelligent speech signal processing so far. Furthermore, methods for acoustic feature extraction, algorithms for classification and regression, as well as end to end deep models are investigated and analysed. Finally, general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

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“…[16] proposes a multi‐task TCN learning model to estimate the degree of depression, combined with related tasks such as emotion and emotion recognition to identify depression. [17] proposes a self‐supervised acoustic feature extraction pre‐training network model for depression which uses a convolutional encoding‐decoding structure and combines it with the LSTM network for depression recognition. The recognition accuracy has been improved to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Identification of depression state based on multi‐scale acoustic features in interrogation environment

Huang

Xiao

et al. 2023

IET Signal Processing

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Depression diagnosis based on speech signals has the advantages of non-invasiveness, low cost, and few restrictions on portability. The research on the recognition of the depression state is carried out based on the acoustic information in the speech signal. Aiming at the interview dialogue speech in the consultation environment, a hierarchical attention temporal convolutional network (HATCN) acoustic depression recognition model is proposed. For sentence acoustic feature learning, a regional attention mechanism is introduced to extract multi-scale sentence features; for segment acoustic feature extraction, the traditional attention mechanism is used to calculate, which is in line with human cognitive mechanism. In addition, a periodic focal loss function is introduced to address the imbalance of positive and negative samples in depression diagnosis. Experiments show that the proposed acoustic depression recognition model has a certain improvement in recognition performance compared with other methods. At the same time, the influence of noise on the recognition of acoustic depression in the real consultation environment is analysed through experiments, and the data enhancement is carried out utilising speech noise, which proves the effectiveness of the data expansion of speech noise.

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Depa

Cited by 28 publications

References 17 publications

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection

Automatic depression recognition by intelligent speech signal processing: A systematic survey

Identification of depression state based on multi‐scale acoustic features in interrogation environment

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