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Thematische Bibliographien / Automatic diagnosis of speech disorder / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Automatic diagnosis of speech disorder“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zhang, Yu, Jing Zhang, Wen Li, Heng Yin, and Ling He. "Automatic Detection System for Velopharyngeal Insufficiency Based on Acoustic Signals from Nasal and Oral Channels." Diagnostics 13, no. 16 (2023): 2714. http://dx.doi.org/10.3390/diagnostics13162714.

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Velopharyngeal insufficiency (VPI) is a type of pharyngeal function dysfunction that causes speech impairment and swallowing disorder. Speech therapists play a key role on the diagnosis and treatment of speech disorders. However, there is a worldwide shortage of experienced speech therapists. Artificial intelligence-based computer-aided diagnosing technology could be a solution for this. This paper proposes an automatic system for VPI detection at the subject level. It is a non-invasive and convenient approach for VPI diagnosis. Based on the principle of impaired articulation of VPI patients,

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Nik Hashim, Nik Nur Wahidah, Nadzirah Ahmad Basri, Mugahed Al-Ezzi Ahmad Ezzi, and Nik Mohd Hazrul Nik Hashim. "Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 238. http://dx.doi.org/10.11591/ijai.v11.i1.pp238-253.

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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

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Nik, Nur Wahidah Nik Hashim, Ahmad Basri Nadzirah, Al-Ezzi Ahmad Ezzi Mugahed, and Mohd Hazrul Nik Hashim Nik. "Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 238–53. https://doi.org/10.11591/ijai.v11.i1.pp238-253.

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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 he

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Sarria Paja, Milton Orlando. "Automatic detection of Parkinson's disease from components of modulators in speech signals." Computer and Electronic Sciences: Theory and Applications 1, no. 1 (2020): 71–82. http://dx.doi.org/10.17981/cesta.01.01.2020.05.

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Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. This disorder mainly affects older adults at a rate of about 2%, and about 89% of people diagnosed with PD also develop speech disorders. This has led scientific community to research information embedded in speech signal from Parkinson's patients, which has allowed not only a diagnosis of the pathology but also a follow-up of its evolution. In recent years, a large number of studies have focused on the automatic detection of pathologies related to the voice, in order to make objective eval

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Mesallam, Tamer A., Mohamed Farahat, Khalid H. Malki, et al. "Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms." Journal of Healthcare Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8783751.

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A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels,

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Anmella, Gerard, Michele De Prisco, Jeremiah B. Joyce, et al. "Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results." Journal of Clinical Medicine 13, no. 17 (2024): 4997. http://dx.doi.org/10.3390/jcm13174997.

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Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitud

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Shrivas, Avinash, Shrinivas Deshpande, Girish Gidaye, et al. "Employing Energy and Statistical Features for Automatic Diagnosis of Voice Disorders." Diagnostics 12, no. 11 (2022): 2758. http://dx.doi.org/10.3390/diagnostics12112758.

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The presence of laryngeal disease affects vocal fold(s) dynamics and thus causes changes in pitch, loudness, and other characteristics of the human voice. Many frameworks based on the acoustic analysis of speech signals have been created in recent years; however, they are evaluated on just one or two corpora and are not independent to voice illnesses and human bias. In this article, a unified wavelet-based paradigm for evaluating voice diseases is presented. This approach is independent of voice diseases, human bias, or dialect. The vocal folds’ dynamics are impacted by the voice disorder, and

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Kruyt, Joanna, and Štefan Beňuš. "Prosodic entrainment in individuals with autism spectrum disorder." Topics in Linguistics 22, no. 2 (2021): 47–61. http://dx.doi.org/10.2478/topling-2021-0010.

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Abstract Entrainment is the tendency of people to behave similarly during an interaction. It occurs on different levels of behaviour, including speech, and has been associated with pro-social behaviour and increased rapport. This review paper outlines the current understanding of linguistic entrainment, particularly at the speech level, in individuals with autism spectrum disorder (ASD), a disorder that is associated with social difficulties and unusual prosody. Aberrant entrainment patterns in individuals with ASD could thus contribute to both their perceived unusual prosody and their social

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Illner, Vojtěch, Tereza Tykalová, Michal Novotný, Jiří Klempíř, Petr Dušek, and Jan Rusz. "Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias." Journal of Speech, Language, and Hearing Research 65, no. 4 (2022): 1386–401. http://dx.doi.org/10.1044/2021_jslhr-21-00549.

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Purpose: This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls. Method: Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their perfo

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Aal, Hadeel Ahmed Abd El, Shereen A. Taie, and Nashwa El-Bendary. "An optimized RNN-LSTM approach for parkinson’s disease early detection using speech features." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2503–12. http://dx.doi.org/10.11591/eei.v10i5.3128.

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Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early d

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Tawhid, Md Nurul Ahad, Siuly Siuly, Hua Wang, Frank Whittaker, Kate Wang, and Yanchun Zhang. "A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG." PLOS ONE 16, no. 6 (2021): e0253094. http://dx.doi.org/10.1371/journal.pone.0253094.

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Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective

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Srilakshmi, Garaga, Vadakattu Sai Harsha, Kurakula Nitin, Bera Vamsi Krishna, and Osipilli David Raju. "Dysarthria Detection and Speech-to-Text Transcription Using Deep Learning and Audio Processing." Journal of Neonatal Surgery 14, no. 6S (2025): 567–73. https://doi.org/10.52783/jns.v14.2276.

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Dysarthria is a motor speech disorder affecting articulation, pitch, and rhythm due to neurological damage in the human body. Early detection is crucial for effective therapy. This study presents a novel dysarthria detection approach using Mel Frequency Logarithmic Spectrograms (MFLS) and Deep Convolutional Neural Networks (DCNN). Speech signals are preprocessed to extract MFLS, capturing essential frequency and temporal features. These spectrograms serve as input to a DCNN, which identifies patterns associated with dysarthric speech. The model was trained on publicly available datasets, achie

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Müller-Eberstein, Max, Dianna Yee, Karren Yang, Gautam Varma Mantena, and Colin Lea. "Hypernetworks for Personalizing ASR to Atypical Speech." Transactions of the Association for Computational Linguistics 12 (2024): 1182–96. http://dx.doi.org/10.1162/tacl_a_00696.

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Abstract Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for—the diagnosis of which requires expert knowledge that is not always available. Even given this knowledge, data scarcity and high inter-/intra-speaker variability further limit the effectiveness of traditional fine-tuning. To circumvent these challenges, we first identify the minimal set of model parameters req

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Filipowicz, Piotr, and Bozena Kostek. "Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set." Applied Sciences 13, no. 10 (2023): 6192. http://dx.doi.org/10.3390/app13106192.

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This work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning methods used in automated stuttering classifications as well as databases and features used. Then, two classical algorithms (k-NN (k-nearest neighbor) and SVM (suppor

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Walker, Traci, Heidi Christensen, Bahman Mirheidari, et al. "Developing an intelligent virtual agent to stratify people with cognitive complaints: A comparison of human–patient and intelligent virtual agent–patient interaction." Dementia 19, no. 4 (2018): 1173–88. http://dx.doi.org/10.1177/1471301218795238.

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Previous work on interactions in the memory clinic has shown that conversation analysis can be used to differentiate neurodegenerative dementia from functional memory disorder. Based on this work, a screening system was developed that uses a computerised ‘talking head’ (intelligent virtual agent) and a combination of automatic speech recognition and conversation analysis-informed programming. This system can reliably differentiate patients with functional memory disorder from those with neurodegenerative dementia by analysing the way they respond to questions from either a human doctor or the

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Boyle, Liam, Petra Helmholz, Derek D. Lichti, and Roslyn Ward. "Validation of Camera Networks Used for the Assessment of Speech Movements." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 41–48. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-41-2024.

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Abstract. The term speech sound disorder describes a range of speech difficulties in children that affect speech intelligibility. Differential diagnosis is difficult and reliant on access to validated and reliable measures. Technological advances aim to provide clinical access to measurements that have been identified as beneficial in diagnosing speech disorders. To generate objective measurements and, consequently, automatic scores, the output from multi-camera networks is required to produce quality results. The quality of photogrammetric results is usually expressed in terms of the precisio

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Terriza, Miguel, Jorge Navarro, Irene Retuerta, et al. "Use of Laughter for the Detection of Parkinson’s Disease: Feasibility Study for Clinical Decision Support Systems, Based on Speech Recognition and Automatic Classification Techniques." International Journal of Environmental Research and Public Health 19, no. 17 (2022): 10884. http://dx.doi.org/10.3390/ijerph191710884.

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Parkinson’s disease (PD) is an incurable neurodegenerative disorder which affects over 10 million people worldwide. Early detection and correct evaluation of the disease is critical for appropriate medication and to slow the advance of the symptoms. In this scenario, it is critical to develop clinical decision support systems contributing to an early, efficient, and reliable diagnosis of this illness. In this paper we present a feasibility study for a clinical decision support system for the diagnosis of PD based on the acoustic characteristics of laughter. Our decision support system is based

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Pandey, Sandeep Kumar, Hanumant Singh Shekhawat, S. R. M. Prasanna, Shalendar Bhasin, and Ravi Jasuja. "A deep tensor-based approach for automatic depression recognition from speech utterances." PLOS ONE 17, no. 8 (2022): e0272659. http://dx.doi.org/10.1371/journal.pone.0272659.

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Depression is one of the significant mental health issues affecting all age groups globally. While it has been widely recognized to be one of the major disease burdens in populations, complexities in definitive diagnosis present a major challenge. Usually, trained psychologists utilize conventional methods including individualized interview assessment and manually administered PHQ-8 scoring. However, heterogeneity in symptomatic presentations, which span somatic to affective complaints, impart substantial subjectivity in its diagnosis. Diagnostic accuracy is further compounded by the cross-sec

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Bauer, Jonathan F., Maurice Gerczuk, Lena Schindler-Gmelch, et al. "Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care." Depression and Anxiety 2024 (April 9, 2024): 1–12. http://dx.doi.org/10.1155/2024/9667377.

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New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder (MDD) during and after treatment. To test this hypothesis, we collected 550 speech samples from telephone-based clinical interviews with 267 individuals in routine care. With this data, we trained and evaluated a machine learning system to identify the absence/presence of a MDD diagnosis (as assessed with the Structured Clinical Interview for DSM-IV) from paralinguistic speech characteristics. Our system classified diagnostic status of MDD with a

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Chui, Kwok Tai, Miltiadis D. Lytras, and Pandian Vasant. "Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset." Applied Sciences 10, no. 13 (2020): 4571. http://dx.doi.org/10.3390/app10134571.

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The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10%. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech-language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detect

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Azis, Putri Alysia, Tenriola Andi, Dewi Fatmarani Surianto, Nur Azizah Eka Budiarti, Andi Akram Nur Risal, and Zulhajji Zulhajji. "Word2Vec Approaches in Classifying Schizophrenia Through Speech Pattern." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 2 (2025): 283–95. https://doi.org/10.29207/resti.v9i2.6323.

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Schizophrenia is a chronic brain disorder characterized by symptoms such as delusions, hallucinations, and disorganized speech, posing significant challenges for accurate diagnosis. This research investigates an innovative Natural Language Processing (NLP) framework for classifying the speech patterns of schizophrenia patients using Word2Vec, with the aim of determining whether there are significant differences between the two features. The dataset comprises speech transcriptions from 121 schizophrenia patients and 121 non-schizophrenia participants collected through structured interviews. Thi

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Juan, Camilo Vasquez-Correa, Arias-Vergara Tomas, Schuster Maria, Rafael Orozco-Arroyave Juan, and Nöth Elmar. "Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson's Disease and Cleft Lip and Palate." Speech Communication 122 (July 29, 2020): 56–67. https://doi.org/10.1016/j.specom.2020.07.005.

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Speech signals may contain different paralinguistic aspects such as the presence of pathologies that affect the proper communication capabilities of a speaker. Those speech disorders have different origin depending on the type of the disease. For instance, diseases with morphological origin such as cleft lip and palate that causes hypernasality, or with neurodegenerative origin such as Parkinson’s disease that generates hypokinetic dysarthria on the patients. Automatic assessment of pathological speech allows to support the diagnosis and/or the evaluation of the disease severity. Convent

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Panek, Daria, Andrzej Skalski, Janusz Gajda, and Ryszard Tadeusiewicz. "Acoustic analysis assessment in speech pathology detection." International Journal of Applied Mathematics and Computer Science 25, no. 3 (2015): 631–43. http://dx.doi.org/10.1515/amcs-2015-0046.

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Abstract Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis) using the a, i and u vowels, spoken

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Fougeron, Cécile, Ina Kodrasi, and Marina Laganaro. "Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s." Brain Sciences 12, no. 11 (2022): 1471. http://dx.doi.org/10.3390/brainsci12111471.

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For the clinical assessment of motor speech disorders (MSDs) in French, the MonPaGe-2.0.s protocol has been shown to be sensitive enough to diagnose mild MSD based on a combination of acoustic and perceptive scores. Here, we go a step further by investigating whether these scores—which capture deviance on intelligibility, articulation, voice, speech rate, maximum phonation time, prosody, diadochokinetic rate—contribute to the differential diagnosis of MSDs. To this aim, we trained decision trees for two-class automatic classification of different pairs of MSD subtypes based on seven deviance s

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Beavis, Lizzie, Ronan O'Malley, Bahman Mirheidari, Heidi Christensen, and Daniel Blackburn. "How can automated linguistic analysis help to discern functional cognitive disorder from healthy controls and mild cognitive impairment?" BJPsych Open 7, S1 (2021): S7. http://dx.doi.org/10.1192/bjo.2021.78.

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AimsThe disease burden of cognitive impairment is significant and increasing. The aetiology of cognitive impairment can be structural, such as in mild cognitive impairment (MCI) due to early Alzheimer's disease (AD), or in functional cognitive disorder (FCD), where there is no structural pathology. Many people with FCD receive a delayed diagnosis following invasive or costly investigations. Accurate, timely diagnosis improves outcomes across all patients with cognitive impairment. Research suggests that analysis of linguistic features of speech may provide a non-invasive diagnostic tool. This

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Cantürk, İsmail. "A Feature Driven Intelligent System for Neurodegenerative Disorder Detection: An Application on Speech Dataset for Diagnosis of Parkinson’s Disease." International Journal on Artificial Intelligence Tools 30, no. 03 (2021): 2150011. http://dx.doi.org/10.1142/s0218213021500111.

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Parkinson’s disease (PD) is a prevalent, and progressive neurological disorder. Due to the motor and non-motor symptoms of the disease, it lowers life quality of the patients. Tremor, rigidity, depression, anxiety etc. are among the symptoms. Clinical diagnosis of PD is usually based on appearance of motor features. Additionally, different empirical tests were proposed by scholars for early detection of the disease. It is known that people with PD have speech impairments. Therefore, voice tests are used for early detection of the disease. In this study, an automated machine learning system was

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Mota, Natália, Mauro Copelli, and Sidarta Ribeiro. "S110. INTERESTING HAPPY THOUGHTS: STRUCTURAL, SEMANTIC AND EMOTIONAL ANALYSIS OF PSYCHOTIC SPEECH USING TIME-LIMITED POSITIVE IMAGE NARRATIVES." Schizophrenia Bulletin 46, Supplement_1 (2020): S76. http://dx.doi.org/10.1093/schbul/sbaa031.176.

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Abstract Background Speech and language analysis from free speech protocols has recently provided a discriminative signal, useful for early diagnosis of schizophrenia. Although different aspects of language (such as structural and semantic coherence) have been applied to different contexts using different data collection protocols, we need to standardize a safe and minimum-effort protocol that can reveal discriminative data, enabling large and remote dataset collection. Also, it is important to understand the correlations between semantic, structural and emotional analysis from the same datase

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Olivares, Rodrigo, Roberto Munoz, Ricardo Soto, et al. "An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease." Applied Sciences 10, no. 5 (2020): 1827. http://dx.doi.org/10.3390/app10051827.

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During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm,

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Sakthivel, S., and V. Prabhu. "Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy." Journal of Healthcare Engineering 2022 (October 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/4248938.

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The use of high-speed video-endoscopy (HSV) in the study of phonatory processes linked to speech needs the precise identification of vocal fold boundaries at the time of vibration. The HSV is a unique laryngeal imaging technology that captures intracycle vocal fold vibrations at a higher frame rate without the need for auditory inputs. The HSV is also effective in identifying the vibrational characteristics of the vocal folds with an increased temporal resolution during retained phonation and flowing speech. Clinically significant vocal fold vibratory characteristics in running speech can be r

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Borovikova, Daria, Oleg Grishin, Anastasia Nenko, et al. "Development of a hardware and software complex for speech analysis and correction." Analysis and data processing systems, no. 2 (June 18, 2021): 135–45. http://dx.doi.org/10.17212/2782-2001-2021-2-135-145.

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In recent years, there has been a dramatic increase in the number of people suffering from functional disorders of voice, usually caused by a psychoemotional stress. Such disorders bring significant discomfort to a person's life as they reduce their communication and social adaptation capacitty, which in turn increases the psychoemotional load. As a result, functional disorders are fixed by the vicious circle mechanism o and can be transformed into the pathology of the speech apparatus. The main method of diagnosis remains expert assessment, which directly depends on the professional skills of

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Alemami, Yahia, and Laiali Almazaydeh. "Pathological Voice Signal Analysis Using Machine Learning Based Approaches." Computer and Information Science 11, no. 1 (2017): 8. http://dx.doi.org/10.5539/cis.v11n1p8.

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Voice signal analysis is becoming one of the most significant examination in clinical practice due to the importance of extracting related parameters to reflect the patient's health. In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease (PD). PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control. Tremor, movement and speech disorders are the main

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Wadle, Lisa-Marie, Ulrich W. Ebner-Priemer, Jerome C. Foo, et al. "Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study." JMIR Mental Health 11 (January 18, 2024): e49222. http://dx.doi.org/10.2196/49222.

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Background The use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs. Objective Previous research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessment

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Zakariah, Mohammed, Reshma B, Yousef Ajmi Alothaibi, Yanhui Guo, Kiet Tran-Trung, and Mohammad Mamun Elahi. "An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks." Computational and Mathematical Methods in Medicine 2022 (April 4, 2022): 1–15. http://dx.doi.org/10.1155/2022/7814952.

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Diseases of internal organs other than the vocal folds can also affect a person’s voice. As a result, voice problems are on the rise, even though they are frequently overlooked. According to a recent study, voice pathology detection systems can successfully help the assessment of voice abnormalities and enable the early diagnosis of voice pathology. For instance, in the early identification and diagnosis of voice problems, the automatic system for distinguishing healthy and diseased voices has gotten much attention. As a result, artificial intelligence-assisted voice analysis brings up new pos

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Zebidi, Hadjer, Zeineb Benmessaoud, and Mondher FRIKHA. "Investigation of Acoustic Features and Machine Learning for Early Detection of Parkinson's Disease." Tuijin Jishu/Journal of Propulsion Technology 45, no. 1 (2024): 942–55. https://doi.org/10.5281/zenodo.13743320.

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Parkinson's disease (PD) is a neurodegenerative disease that afflicts millions of people. The early detection of the disease is crucial. According to recent research, the level of dysarthria is a good indicator for computer-assisted diagnosis and remote monitoring of patients in the early phases. Despite the significance of articulatory deficits in dysarthria among individuals with PD, automatic speech performance evaluation methods mainly concentrate on assessing dysphonia. In this study, our objective was to classify the phonation, articulation et diadochokinetic features by machine learning

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Kang, Hahn, Fangxu Xing, Imani Gilbert, et al. "Multi-atlas-based segmentation of vocal tract anatomy for children using label fusion techniques from dynamic MRI." Journal of the Acoustical Society of America 156, no. 4_Supplement (2024): A49. https://doi.org/10.1121/10.0035066.

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Segmentation of oral and velopharyngeal anatomy is essential for quantifying anatomical structures and analyzing the dynamic and discrete movements of these structures during speech production. This aids in the diagnosis and treatment of speech-related disorders as the different speech articulators are able to be analyzed independently and in relation to their co-articulatory movements. Direct visualization of structures and musculature important for speech production is best attained using MRI. Manual segmentation, however, is labor-intensive and often suffers from poor reproducibility, requi

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Jahan, Sultana. "83 Catatonia in a 17-year-old Male Patient with Bipolar Disorder, a Case Study." CNS Spectrums 24, no. 1 (2019): 217. http://dx.doi.org/10.1017/s1092852919000622.

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AbstractStudy Objective(s)Catatonia is not only present in adults; children & adolescents can suffer from catatonia but are often misdiagnosed. A study by Ghaziuddin, Dhossche and Marcotte (2012) found that 18 of the 101 child and adolescent patients had symptoms of catatonia, but only 2 actually had been given a diagnosis by their providers.Method17-year-old male who was recently discharged from the inpatient psychiatric unit with the diagnosis of Major Depressive Disorder. His discharge medication was bupropion XL 150mg daily. Within 10 days of his discharge, he was back to the emergency

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Novaes, Laura Nunes, Maria Letícia Pereira Da Silva, and Franco Andrei da Cunha Junqueira. "Considerations about pedophilia and child sexual abuse speeches: concepts and differentiations." Revista Caribeña de Ciencias Sociales 13, no. 2 (2024): e3605. http://dx.doi.org/10.55905/rcssv13n2-010.

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The article discusses the confusion prevalent on social media between pedophilia as a mental disorder and the crime of child sexual abuse. The lack of distinction between individuals with pedophilic disorder and aggressors without such a disorder is highlighted. The study uses Twitter as a source of analysis, collecting popular tweets between December 2019 and July 2020. The methodology combines literature review, exploring medical and legal concepts, and analysis of speeches on Twitter. The results highlight the mistaken association between pedophilia and crime, with opinions emphasizing the

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Song, Joomee, Ju Hwan Lee, Jungeun Choi, et al. "Detection and differentiation of ataxic and hypokinetic dysarthria in cerebellar ataxia and parkinsonian disorders via wave splitting and integrating neural networks." PLOS ONE 17, no. 6 (2022): e0268337. http://dx.doi.org/10.1371/journal.pone.0268337.

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Dysarthria may present during the natural course of many degenerative neurological conditions. Hypokinetic and ataxic dysarthria are common in movement disorders and represent the underlying neuropathology. We developed an artificial intelligence (AI) model to distinguish ataxic dysarthria and hypokinetic dysarthria from normal speech and differentiate ataxic and hypokinetic speech in parkinsonian diseases and cerebellar ataxia. We screened 804 perceptual speech analyses performed in the Samsung Medical Center Neurology Department between January 2017 and December 2020. The data of patients di

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Tonn, Peter, Yoav Degani, Shani Hershko, Amit Klein, Lea Seule, and Nina Schulze. "Development of a Digital Content-Free Speech Analysis Tool for the Measurement of Mental Health and Follow-Up for Mental Disorders: Protocol for a Case-Control Study." JMIR Research Protocols 9, no. 5 (2020): e13852. http://dx.doi.org/10.2196/13852.

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Background The prevalence of mental disorders worldwide is very high. The guideline-oriented care of patients depends on early diagnosis and regular and valid evaluation of their treatment to be able to quickly intervene should the patient’s mental health deteriorate. To ensure effective treatment, the level of experience of the physician or therapist is of importance, both in the initial diagnosis and in the treatment of mental illnesses. Nevertheless, experienced physicians and psychotherapists are not available in enough numbers everywhere, especially in rural areas or in less developed cou

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Geng, Lei, Hongfeng Shan, Zhitao Xiao, Wei Wang, and Mei Wei. "Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method." Biomedical Engineering / Biomedizinische Technik 66, no. 6 (2021): 613–25. http://dx.doi.org/10.1515/bmt-2021-0112.

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Abstract Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to e

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Hamza, Amina, and Djamel Addou. "Dysarthria Severity detection Using Recurrent and Convolutional Neural Networks." AL-Lisaniyyat 30, no. 2 (2024): 27–39. https://doi.org/10.61850/allj.v30i2.736.

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The diagnosis and monitoring of dysarthria, a speech disorder caused by neuro-motor problems that affect articulation, depend on a precise evaluation of its severity. When creating automated systems to identify and categorize dysarthric speech, accurate severity classification is essential. Using neural network models, specifically recurrent neural networks (RNN) and convolutional neural networks (CNN), this paper offers a thorough investigation of how to distinguish dysarthric voices among a collection of normal voice samples and categorize the severity of dysarthria. Among the features used

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Loh, Hui Wen, Wanrong Hong, Chui Ping Ooi, et al. "Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)." Sensors 21, no. 21 (2021): 7034. http://dx.doi.org/10.3390/s21217034.

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease

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Demir, Fatih, Kamran Siddique, Mohammed Alswaitti, Kursat Demir, and Abdulkadir Sengur. "A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection." Journal of Personalized Medicine 12, no. 1 (2022): 55. http://dx.doi.org/10.3390/jpm12010055.

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Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed ca

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Mohammed, Mazin Abed, Karrar Hameed Abdulkareem, Salama A. Mostafa, et al. "Voice Pathology Detection and Classification Using Convolutional Neural Network Model." Applied Sciences 10, no. 11 (2020): 3723. http://dx.doi.org/10.3390/app10113723.

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Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the app

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Zavadenko, Nikolay N. "Dyslexia as the most prevalent form of specific learning disabilities." L.O. Badalyan Neurological Journal 2, no. 3 (2021): 146–58. http://dx.doi.org/10.46563/2686-8997-2021-2-3-146-158.

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Dyslexia is the most common form of specific learning disabilities. Dyslexia is observed in 5-17.5 % of schoolchildren, and among children with specific learning disabilities, it accounts for about 70-80 %. Usually, dyslexia manifests itself as the inability to achieve an appropriate level of reading skills development that would be proportional to their intellectual abilities and writing and spelling skills. Secondary consequences of dyslexia may include problems in reading comprehension and reduced reading experience that can impede the growth of vocabulary and background skills. The review

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Lopes da Cunha, Pamela, Fabián Ruiz, Franco Ferrante, et al. "Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia." PLOS ONE 19, no. 6 (2024): e0304272. http://dx.doi.org/10.1371/journal.pone.0304272.

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Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated t

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Lee, Jung Hyuk, Geon Woo Lee, Guiyoung Bong, Hee Jeong Yoo, and Hong Kook Kim. "Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation." Sensors 20, no. 23 (2020): 6762. http://dx.doi.org/10.3390/s20236762.

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Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characte

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Kist, Andreas M., Pablo Gómez, Denis Dubrovskiy, et al. "A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis." Journal of Speech, Language, and Hearing Research 64, no. 6 (2021): 1889–903. http://dx.doi.org/10.1044/2021_jslhr-20-00498.

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Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold oscillations by segmenting the glottal area. This challenging task has been tackled by various studies; however, the proposed approaches are mostly limited and not suitable for daily clinical routine. Method We developed a user-friendly software in C# that allows the editing, motion correction, segmentation, and quantitative analysis of HSV data. We f

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Dahmani, Mohamed, and Mhania Guerti. "Recurrence Quantification Analysis of Glottal Signal as non Linear Tool for Pathological Voice Assessment and Classification." International Arab Journal of Information Technology 17, no. 6 (2020): 857–66. http://dx.doi.org/10.34028/iajit/17/6/4.

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Automatic detection and assessment of Vocal Folds pathologies using signal processing techniques knows an extensively challenge use in the voice or speech research community. This paper contributes the application of the Recurrence Quantification Analysis (RQA) to a glottal signal waveform in order to evaluate the dynamic process of Vocal Folds (VFs) for diagnosis and classify the voice disorders. The proposed solution starts by extracting the glottal signal waveform from the voice signal through an inverse filtering algorithm. In the next step, the parameters of RQA are determined via the Rec

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Robillard, Manon, Annie Roy-Charland, and Sylvie Cazabon. "The Role of Cognition on Navigational Skills of Children and Adolescents With Autism Spectrum Disorders." Journal of Speech, Language, and Hearing Research 61, no. 7 (2018): 1579–90. http://dx.doi.org/10.1044/2018_jslhr-s-17-0206.

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Purpose This study examined the role of cognition on the navigational process of a speech-generating device (SGD) among individuals with a diagnosis of autism spectrum disorder (ASD). The objective was to investigate the role of various cognitive factors (i.e., cognitive flexibility, sustained attention, categorization, fluid reasoning, and working memory) on the ability to navigate an SGD with dynamic paging and taxonomic grids in individuals with ASD. Method Twenty individuals aged 5 to 20 years with ASD were assessed using the Leiter International Performance Scale–Revised (Roid & Mille

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