Specific Language Impairment Detection Through Voice Analysis

Slogrove, Kayleigh; Haar, Dustin van der

doi:10.1007/978-3-030-53337-3_10

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…The kernel extreme learning machine were trained with the speech signals, and it performed an accuracy of 99.41% on the test data. Apart from MFCC, in [ 89 ], Tonnetz and Chroma were calculated, combined with SVM, RF and Recurrent Neural Network to detect SLI. The Tonnetz and Chroma reached accuracies of 70% and 71%, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Sch-net: a deep learning architecture for automatic detection of schizophrenia

Jia

Yang

et al. 2021

BioMed Eng OnLine

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Background Schizophrenia is a chronic and severe mental disease, which largely influences the daily life and work of patients. Clinically, schizophrenia with negative symptoms is usually misdiagnosed. The diagnosis is also dependent on the experience of clinicians. It is urgent to develop an objective and effective method to diagnose schizophrenia with negative symptoms. Recent studies had shown that impaired speech could be considered as an indicator to diagnose schizophrenia. The literature about schizophrenic speech detection was mainly based on feature engineering, in which effective feature extraction is difficult because of the variability of speech signals. Methods This work designs a novel Sch-net neural network based on a convolutional neural network, which is the first work for end-to-end schizophrenic speech detection using deep learning techniques. The Sch-net adds two components, skip connections and convolutional block attention module (CBAM), to the convolutional backbone architecture. The skip connections enrich the information used for the classification by emerging low- and high-level features. The CBAM highlights the effective features by giving learnable weights. The proposed Sch-net combines the advantages of the two components, which can avoid the procedure of manual feature extraction and selection. Results We validate our Sch-net through ablation experiments on a schizophrenic speech data set that contains 28 patients with schizophrenia and 28 healthy controls. The comparisons with the models based on feature engineering and deep neural networks are also conducted. The experimental results show that the Sch-net has a great performance on the schizophrenic speech detection task, which can achieve 97.68% accuracy on the schizophrenic speech data set. To further verify the generalization of our model, the Sch-net is tested on open access LANNA children speech database for specific language impairment detection. The results show that our model achieves 99.52% accuracy in classifying patients with SLI and healthy controls. Our code will be available at https://github.com/Scu-sen/Sch-net. Conclusions Extensive experiments show that the proposed Sch-net can provide aided information for the diagnosis of schizophrenia and specific language impairment.

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Section: Resultsmentioning

confidence: 99%

“…The Tonnetz and Chroma reached accuracies of 70% and 71%, respectively. In the four studies [ 85 – 89 ], high accuracies had been achieved for speaker-dependent classification.…”

Section: Resultsmentioning

confidence: 99%

Sch-net: a deep learning architecture for automatic detection of schizophrenia

Jia

Yang

et al. 2021

BioMed Eng OnLine

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“…The Tonnetz and Chroma reached accuracies of 70% and 71%, respectively. In the four studies [92,93,94,95,96], high accuracies had been achieved for speaker-dependent classification.…”

Section: Network Visualization Using Grad-cammentioning

confidence: 98%

“…Previous studies [92,93,94,95,96,97,98] had demonstrated that speech can be viewed as a symbol of diagnosing SLI. In [92,93,94], 1582 acoustic features were extracted from 34 low-level descriptors and its 21 statistical functionals.…”

Section: Network Visualization Using Grad-cammentioning

confidence: 99%

“…The kernel extreme learning machine trained with the speech signals, and it outperformed an accuracy of 99.41% on the test data. Apart from MFCC, in [96], Tonnetz and Chroma were calculated, combined with SVM, RF and Recurrent Neural Network to detect SLI. The Tonnetz and Chroma reached accuracies of 70% and 71%, respectively.…”

Section: Network Visualization Using Grad-cammentioning

confidence: 99%

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Sch-net: A Deep Learning Architecture for Automatic Schizophrenia Setection

Yang

et al. 2021

Preprint

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Background: Schizophrenia is a chronic and severe mental disease, which largely influences the daily life and work of patients. In the clinic, schizophrenia with negative symptoms is usually misdiagnosed and hardly treated. The diagnosis is also dependent on the experience of clinicians. It is urgent to develop an objective and effective method to diagnose schizophrenia with negative symptoms. Recent studies had shown that impaired speech could be considered as an indicator to diagnose schizophrenia. The literature about schizophrenia speech detection was mainly based on feature engineering, in which effective feature extraction is difficult because of the variability of speech signals. Methods: A novel deep learning architecture based on a convolutional neural network, termed Sch-net, is designed for end-to-end schizophrenia speech detection in this work. It avoids the procedure of artificial feature extraction and combines the advantages of skip connections and attention mechanism to discriminate schizophrenia patients and controls. Results: We validate our Sch-net through ablation experiments on a schizophrenia speech dataset that contains 28 schizophrenia patients and 28 healthy controls. The comparisons with the models based on feature engineering and classic deep neural networks are also conducted. The experimental results show that the Sch-net has a great performance on schizophrenia speech detection task, which can achieve 97.76% accuracy on the schizophrenia speech dataset. To further verify the generalization of our model, the Sch-net is tested on open access LANNA children speech database for specific language impairment detection. Our code is available at https://github.com/Scu-sen/Sch-net. Conclusions: Extensive experiments show that the proposed Sch-net can provide the aided information for the diagnosis of schizophrenia speech and specific language impairment.

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Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Barua

Aydemir

Doğan

et al. 2022

Neural Comput & Applic

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Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

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Specific Language Impairment Detection Through Voice Analysis

Cited by 5 publications

References 7 publications

Sch-net: a deep learning architecture for automatic detection of schizophrenia

Sch-net: a deep learning architecture for automatic detection of schizophrenia

Sch-net: A Deep Learning Architecture for Automatic Schizophrenia Setection

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

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