Detecting Developmental Dysphasia in Children using Speech Data

Ramarao, D; Singh, Chaman; Shahnawazuddin, Syed; Adiga, Nagaraj; Pradhan, Gayadhar

doi:10.1109/spcom.2018.8724441

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Once identified, a speech and language therapist (SLT) can be consulted to evaluate and treat the children. To the best of our knowledge, there are only two prior works in automatic SLI detection from speech signals [13], [14]. In [13], the authors presented the first publicly open speech database (detailed in Section-III) for investigating the effects of speech problems in children with SLI.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in [13], features extracted using the openSMILE toolkit [16] were used to detect SLI. In [14], extreme learning machines (ELMs) trained using Gaussian posteriograms learned on frame-level Mel-frequency cepstral coefficients (MFCCs) were used for identifying children with SLI. In the previous two studies, the authors have reported high classification accuracies (> 96%); however, they have not studied the robustness of the considered features for speaker-independent classification.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Specific Language Impairment in Children Using Glottal Source Features

2020

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Developmental dysphasia, also known as specific language impairment (SLI), is a language disorder in children that involves difficulty in speaking and understanding spoken words. Detecting SLI at an early stage is very important for successful speech therapy in children. In this paper, we propose a novel approach based on glottal source features for detecting children with SLI using the speech signal. The proposed method utilizes time-and frequency-domain glottal parameters, which are extracted from the voice source signal obtained using glottal inverse filtering (GIF). In addition, Mel-frequency cepstral coefficient (MFCC) and openSMILE based acoustic features are also extracted from speech utterances. Two machine learning algorithms, namely, support vector machine (SVM) and feed-forward neural network (FFNN), are trained separately for the MFCC, openSMILE and glottal features. A leave-fourteen-speakers-out cross-validation strategy is used for evaluating the classifiers. The experiments are conducted using the SLI speech corpus launched by the LANNA research group. Experimental results show that the glottal parameters contain significant discriminative information required for identifying children with SLI. Furthermore, the complementary nature of glottal parameters is investigated by independently combining these features with the MFCC and openSMILE acoustic features. The overall results indicate that the glottal features when used in combination with MFCC feature set provides the best performance with the FFNN classifier in the speaker-independent scenario. INDEX TERMS Developmental dysphasia, openSMILE, glottal source parameters, support vector machines, artificial neural networks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Specific Language Impairment in Children Using Glottal Source Features

2020

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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 features were given as input to SVM, achieving 96.94% accuracy on the LANNA children speech database. In [95], Gaussian posteriorgrams trained on MFCC features were employed to discriminate children with SLI and healthy controls. The kernel extreme learning machine trained with the speech signals, and it outperformed an accuracy of 99.41% on the test data.…”

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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Detecting Developmental Dysphasia in Children using Speech Data

Cited by 10 publications

References 13 publications

Detection of Specific Language Impairment in Children Using Glottal Source Features

Detection of Specific Language Impairment in Children Using Glottal Source Features

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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