Relevant bibliographies by topics / Automatic diagnosis of speech disorder

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Academic literature on the topic 'Automatic diagnosis of speech disorder'

Author: Grafiati
Published: 28 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Automatic diagnosis of speech disorder"

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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 (December 14, 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 evaluations of the voice in a non-invasive manner. In cases where the pathology primarily affects the vibratory patterns of vocal folds such as Parkinson's, the analyses typically performed are sustained over vowel pronunciations. In this article, it is proposed to use information from slow and rapid variations in speech signals, also known as modulating components, combined with an effective dimensionality reduction reduction approach that will be used as input to the classification system. The proposed approach achieves classification rates higher than 88%, surpassing the classical approach based on mel cepstrals coefficients (MFCC). The results show that the information extracted from slow varying components is highly discriminative for the task at hand, and could support assisted diagnosis systems for PD.
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Mesallam, Tamer A., Mohamed Farahat, Khalid H. Malki, Mansour Alsulaiman, Zulfiqar Ali, Ahmed Al-nasheri, and Ghulam Muhammad. "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, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database.
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Walker, Traci, Heidi Christensen, Bahman Mirheidari, Thomas Swainston, Casey Rutten, Imke Mayer, Daniel Blackburn, and Markus Reuber. "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 (September 14, 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 intelligent virtual agent. However, much of this computerised analysis has relied on simplistic, nonlinguistic phonetic features such as the length of pauses between talk by the two parties. To gain confidence in automation of the stratification procedure, this paper investigates whether the patients’ responses to questions asked by the intelligent virtual agent are qualitatively similar to those given in response to a doctor. All the participants in this study have a clear functional memory disorder or neurodegenerative dementia diagnosis. Analyses of patients’ responses to the intelligent virtual agent showed similar, diagnostically relevant sequential features to those found in responses to doctors’ questions. However, since the intelligent virtual agent’s questions are invariant, its use results in more consistent responses across people – regardless of diagnosis – which facilitates automatic speech recognition and makes it easier for a machine to learn patterns. Our analysis also shows why doctors do not always ask the same question in the exact same way to different patients. This sensitivity and adaptation to nuances of conversation may be interactionally helpful; for instance, altering a question may make it easier for patients to understand. While we demonstrate that some of what is said in such interactions is bound to be constructed collaboratively between doctor and patient, doctors could consider ensuring that certain, particularly important and/or relevant questions are asked in as invariant a form as possible to be better able to identify diagnostically relevant differences in patients’ responses.
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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 (June 25, 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, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.
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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 (July 1, 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 detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c-means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c-means clustering. Lastly, the proposed CGAN-IFCM outperforms existing models in its true negative rate and true positive rate by 9.9–12.9% and 9.1–44.8%, respectively.
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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 (June 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 study aimed to investigate the linguistic differences in conversations between people with early signs of cognitive impairment with and without structural pathology, with a view to developing a screening tool using linguistic analysis of conversations.MethodIn this explorative, cross-sectional study, we recruited 25 people with MCI considered likely due to AD, (diagnosed according to Petersen's criteria and referred to as PwMCI), 25 healthy controls (HCs) and 15 people with FCD (PwFCD). Participants’ responses to a standard questionnaire asked by an interactional virtual agent (Digital Doctor) were quantified using previously identified parameters. This paper presents statistical analyses of the responses and a discussion of the results.ResultPwMCI produced fewer words than PwFCD and HCs. The ratio of pauses to speech was generally lower for PwMCI and PwFCD than for HCs. PwMCI showed a greater pause to speech ratio for recent questions (such as ‘what did you do at the weekend?’) compared with the HCs. Those with FCD showed the greatest pause to speech ratio in remote memory questions (such as ‘what was your first job?’). The average age of acquisition of answers for verbal fluency questions was lower in the MCI group than HCs.ConclusionThe results and qualitative observations support the relative preservation of remote memory compared to recent memory in MCI due to AD and decreased spontaneous elaboration in MCI compared with healthy controls and patients with FCD. Word count, age of acquisition and pause to speech ratio could form part of a diagnostic toolkit in identifying those with structural and functional causes of cognitive impairment. Further investigation is required using a large sample.
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Di Matteo, Daniel, Wendy Wang, Kathryn Fotinos, Sachinthya Lokuge, Julia Yu, Tia Sternat, Martin A. Katzman, and Jonathan Rose. "Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study." JMIR Formative Research 5, no. 1 (January 29, 2021): e22723. http://dx.doi.org/10.2196/22723.

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Background The ability to objectively measure the severity of depression and anxiety disorders in a passive manner could have a profound impact on the way in which these disorders are diagnosed, assessed, and treated. Existing studies have demonstrated links between both depression and anxiety and the linguistic properties of words that people use to communicate. Smartphones offer the ability to passively and continuously detect spoken words to monitor and analyze the linguistic properties of speech produced by the speaker and other sources of ambient speech in their environment. The linguistic properties of automatically detected and recognized speech may be used to build objective severity measures of depression and anxiety. Objective The aim of this study was to determine if the linguistic properties of words passively detected from environmental audio recorded using a participant’s smartphone can be used to find correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. Methods An Android app was designed to collect periodic audiorecordings of participants’ environments and to detect English words using automatic speech recognition. Participants were recruited into a 2-week observational study. The app was installed on the participants’ personal smartphones to record and analyze audio. The participants also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Words detected from audiorecordings were categorized, and correlations were measured between words counts in each category and the 4 self-report measures to determine if any categories could serve as correlates of social anxiety disorder, generalized anxiety disorder, depression, or general impairment. Results The participants were 112 adults who resided in Canada from a nonclinical population; 86 participants yielded sufficient data for analysis. Correlations between word counts in 67 word categories and each of the 4 self-report measures revealed a strong relationship between the usage rates of death-related words and depressive symptoms (r=0.41, P<.001). There were also interesting correlations between rates of word usage in the categories of reward-related words with depression (r=–0.22, P=.04) and generalized anxiety (r=–0.29, P=.007), and vision-related words with social anxiety (r=0.31, P=.003). Conclusions In this study, words automatically recognized from environmental audio were shown to contain a number of potential associations with severity of depression and anxiety. This work suggests that sparsely sampled audio could provide relevant insight into individuals’ mental health.
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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 (September 1, 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 at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient
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Bone, Daniel, Chi-Chun Lee, Matthew P. Black, Marian E. Williams, Sungbok Lee, Pat Levitt, and Shrikanth Narayanan. "The Psychologist as an Interlocutor in Autism Spectrum Disorder Assessment: Insights From a Study of Spontaneous Prosody." Journal of Speech, Language, and Hearing Research 57, no. 4 (August 2014): 1162–77. http://dx.doi.org/10.1044/2014_jslhr-s-13-0062.

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PurposeThe purpose of this study was to examine relationships between prosodic speech cues and autism spectrum disorder (ASD) severity, hypothesizing a mutually interactive relationship between the speech characteristics of the psychologist and the child. The authors objectively quantified acoustic-prosodic cues of the psychologist and of the child with ASD during spontaneous interaction, establishing a methodology for future large-sample analysis.MethodSpeech acoustic-prosodic features were semiautomatically derived from segments of semistructured interviews (Autism Diagnostic Observation Schedule, ADOS; Lord, Rutter, DiLavore, & Risi, 1999; Lord et al., 2012) with 28 children who had previously been diagnosed with ASD. Prosody was quantified in terms of intonation, volume, rate, and voice quality. Research hypotheses were tested via correlation as well as hierarchical and predictive regression between ADOS severity and prosodic cues.ResultsAutomatically extracted speech features demonstrated prosodic characteristics of dyadic interactions. As rated ASD severity increased, both the psychologist and the child demonstrated effects for turn-end pitch slope, and both spoke with atypical voice quality. The psychologist's acoustic cues predicted the child's symptom severity better than did the child's acoustic cues.ConclusionThe psychologist, acting as evaluator and interlocutor, was shown to adjust his or her behavior in predictable ways based on the child's social-communicative impairments. The results support future study of speech prosody of both interaction partners during spontaneous conversation, while using automatic computational methods that allow for scalable analysis on much larger corpora.
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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 (May 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 proposed for high accuracy classification of the speech signals of PD patients. The system includes feature reduction methods and classification algorithms. Feature reductions and classifications were performed for all participants, males, and females separately. Contributions of feature sets to classification accuracy were discussed. Experimental results were evaluated with different performance metrics. The proposed system obtained state of the art results in all categories. We acquired better performances for gender based classifications.
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Dissertations / Theses on the topic "Automatic diagnosis of speech disorder"

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Bezůšek, Marek. "Objektivizace Testu 3F - dysartrický profil pomocí akustické analýzy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442568.

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Test 3F is used to diagnose the extent of motor speech disorder – dysarthria for czech speakers. The evaluation of dysarthric speech is distorted by subjective assessment. The motivation behind this thesis is that there are not many automatic and objective analysis tools that can be used to evaluate phonation, articulation, prosody and respiration of speech disorder. The aim of this diploma thesis is to identify, implement and test acoustic features of speech that could be used to objectify and automate the evaluation. These features should be easily interpretable by the clinician. It is assumed that the evaluation could be more precise because of the detailed analysis that acoustic features provide. The performance of these features was tested on database of 151 czech speakers that consists of 51 healthy speakers and 100 patients. Statistical analysis and methods of machine learning were used to identify the correlation between features and subjective assesment. 27 of total 30 speech tasks of Test 3F were identified as suitable for automatic evaluation. Within the scope of this thesis only 10 tasks of Test 3F were tested because only a limited part of the database could be preprocessed. The result of statistical analysis is 14 features that were most useful for the test evaluation. The most significant features are: MET (respiration), relF0SD (intonation), relSEOVR (voice intensity – prosody). The lowest prediction error of the machine learning regression models was 7.14 %. The conclusion is that the evaluation of most of the tasks of Test 3F can be automated. The results of analysis of 10 tasks shows that the most significant factor in dysarthria evaluation is limited expiration, monotone voice and low variabilty of speech intensity.
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Heidtke, Uta Johanna. "Diagnosis of Auditory Processing Disorder in Children using an Adaptive Filtered Speech Test." Thesis, University of Canterbury. Communication Disorders, 2010. http://hdl.handle.net/10092/4536.

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Auditory Processing Disorder (APD) is an auditory-specific perceptual deficit in the processing of auditory stimuli that occurs in spite of normal peripheral hearing thresholds and normal intellectual capacity American Speech-Language-Hearing Association (ASHA, 2005). The diagnostic process of APD typically involves a test battery consisting of sub-tests designed to examine the integrity of various auditory processes of the central auditory nervous system (CANS). One category of these sub-tests is the low-pass filtered speech test (FST), whereby a speech signal is distorted by using filtering to modify its frequency content. One limitation of the various versions of the FST currently available is that they are administered using a constant level of low-pass filtering (e.g. a fixed 1 kHz corner frequency) which makes them prone to ceiling and floor effects (Farrer & Keith, 1981). As a consequence, the efficacy and accuracy of these tests is significantly compromised (Martin & Clark, 1977). The purpose of the present study was to counter these effects by utilising the University of Canterbury Adaptive Filtered Speech Test (UCAST) which uses a computer-based adaptive procedure intended to improve the efficiency and sensitivity of the test over its constant-level counterparts. A comprehensive APD test battery was carried out on 18 children aged 7-13 suspected of APD and on an aged-matched control group of 10 children. Fifteen of the APD suspected children were diagnosed with APD based on their performance on a traditional APD test battery, comprising the Compressed and Reverberated Words Test (CRWT), the Double Digits test (DDT), the Frequency Pattern test (FPT) and the Random Gap Detection test (RGDT). In addition, the UCAST was administered to examine whether the low-pass filter limit at which children score 62.5% of words correct i) differed significantly between children who either passed or failed the APD test battery; ii) correlated with their score on the traditional APD battery (TAPDB); and iii) correlated with their score on a commercially available low-pass filtered speech test, the Filtered Words Subtest of SCAN-C (Keith, 2000b). Results demonstrated a significant difference between the UCAST low-pass filter limit at which APD and control children scored 62.5% words correct (two-way repeated measures ANOVA, p < 0.01). Significant correlations were found between the UCAST and three of the four tests used in the TAPD - the DDT, the RGDT and the FPT (Pearson Correlation coefficient, p < 0.01). No correlation was found between the UCAST and the CRWT or between the UCAST and the SCAN-C (FW) test (p > 0.05). These findings provide evidence that an adaptive filtered speech test may discriminate between children with and without APD with greater sensitivity and specificity than its constant-level counterparts.
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Whaley, Jennifer R. "Diagnosis of an Autism Spectrum Disorder: Parents' Perceptions of the Interpretive Conference." Oxford, Ohio : Miami University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1176586163.

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Cogswell, Pamela E. "A Study of the Association Among the Diagnosis of Speech-Language Impairments and the Diagnoses of Learning Disabilities and/or Attention Deficit Hyperactivity Disorder." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4222.

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The purpose of this study was to determine if an association exists among the diagnosis of speech-language impairments (SLI) and the diagnoses of learning disabilities (LD) and/or attention deficit hyperactivity disorder (ADHD) in a school-aged population of children referred to a Learning Disorders Clinic (LDC) because of academic underachievement and/or behavior problems. The two research questions asked in this study are: (a) What percentage of students diagnosed with SLI have a concomitant diagnosis of LD and/or ADHD? and (b) Is there an association among the diagnosis of SLI and the diagnoses of LD and/or ADHD? A sample of 94 subjects was obtained from review of 291 LDC records of children ref erred and diagnosed during the years 1989-1992. The subjects were grouped into eight categories by diagnosis, that is, (a) SLI, (b) SLI/LD, (c) SLI/ADHO, (d) SLI/LO/ADHD, (e) no diagnosis of SLI/LO/AOHD, (f) LO, (g) ADHD, and (h) LD/ADHD. The obtained Chi square value was not statistically significant at a .OS alpha level. Thus, the null hypothesis: there will be no association among the diagnosis of SLI and the diagnoses of LO and/or ADHD, could not be rejected. In this sample, however, 85% of the children diagnosed with SLI had a concomitant diagnosis of LD and/or ADHD, and 70% with no SLI diagnosis were diagnosed with LD and/or ADHD. The overlapping nature of the disorders of SLI, LD, and ADHD is noted. The definitions of SLI and LO demonstrate how enmeshed language and learning problems are. One inference from this study is that as children grow older, their language deficits are recognized in the context of a learning disorder.
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Esteller, Rosana. "Detection of seizure onset in epileptic patients from intracranial EEG signals." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.

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Kwok, Chui-ling Irene, and 郭翠玲. "Electropalatographic investigation of normal Cantonese speech: a qualitative and quantitative analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1992. http://hub.hku.hk/bib/B38626135.

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Amaro, Luciana. "Descrição de distorções dos sons da fala em crianças com e sem transtorno fonológico." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/8/8139/tde-05122008-111037/.

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No transtorno fonológico podem ocorrer concomitantemente alterações fonéticas e fonológicas, que comprometem a articulação e o conhecimento internalizado do sistema de sons da língua. As alterações fonéticas podem acontecer também em crianças com desenvolvimento típico de linguagem. Várias pesquisas têm mostrado a importância de se utilizar técnicas objetivas durante o diagnóstico bem como da aplicação de índices de gravidade. O objetivo desta pesquisa é identificar a ocorrência de distorções de fala e aplicar e comparar os índices de gravidade em crianças entre cinco e sete anos de idade com e sem transtorno fonológico. Para isso, foram avaliadas 30 crianças com desenvolvimento típico de linguagem (GSTF) e 15 crianças com transtorno fonológico (GTF). Foram aplicadas provas experimentais de fonologia, fala espontânea e avaliação da motricidade orofacial e calculados os índices PCC, PCC-R, PDI, RDI e ACI nas provas de fonologia e fala espontânea. Se detectada qualquer tipo de distorção em quaisquer umas das provas de fonologia nos sons [s], [z], [?], [?], [l], [?] e [?] era aplicada a prova para verificação específica de distorção, além da palatografia e linguografia.Os resultados apontaram que no GSTF, 23,3% das crianças apresentou distorção nas provas de fonologia e fala espontânea nos sons [s], [z], [?] e [l]; no GFT 20% das crianças apresentou distorção nas provas de fonologia e fala espontânea nos sons [s], [z] e [?]; não houve evidências de diferença significativa entre o número de sujeitos que apresentaram distorção no GSTF e GTF. Houve diferença significante apenas nas provas de imitação e fala espontânea na faixa etária de sete anos, com maior ocorrência de distorção do [s] no grupo GTF do que no GSTF. Apenas o GTF apresentou distorção no [?], parece que a distorção deste som está mais relacionada ao transtorno fonológico. A análise da palatografia confirmou a análise perceptiva, oferecendo a vantagem de mostrar o local exato da produção. No GSTF e no GTF, os sujeitos que não apresentaram distorção obtiveram todos os índices melhores do que os sujeitos com distorção. O índice ACI indicou que o GSTF sem distorção teve o melhor desempenho, mostrando-se adequado para medir a competência articulatória .
Phonological and phonetic alterations can occur together in the phonological disorder compromising the articulation and the internal knowledge of the speech sounds of a language. Phonetic alterations can occur in children with typical phonological development. Several researches have shown the importance of the use of objective techniques during both the diagnosis of disorder and application of severity indexes. The aim of this research is to identify the occurrence of distortions in the speech. Also, to apply and compare severity indexes in children between five and seven years old with and without phonological disorders. 30 children with typical language development (GSTF) and 15 phonologically-disordered children (GTF) were assessed. Experimental test of Phonology (nomeation, imitation and continuous speech) and the oral motricity were applied. The PCC, PPC-R, PDI, RDI and ACI indexes were calculated based on Phonology tests. If any kind of distortion as detected in any Phonology test related to the sounds [s], [z], [?], [?], [l], [?] and [?], the specific test to verify distortion was used, for confirmation and perceptual classification of kind distortion, besides the palatography and tongue graph. The results pointed that in the GSTF, 23,3% of the children presented distortion in the Phonology tests and continuous speech in the [s], [z] and [?] sounds; there was no evidence of statistic differences between number of subjects that presented distortion in the GSTF and in the GTF. There was significant difference only in the imitation and continuous speech tests in the age range of seven years, with more occurrence of [s] distortion in the GTF compared to the GSTF. Only the GTF presented [] distortions. It seems that this distortion is more related to the phonological disorder. The palatography and the tongue graph confirmed the perceptive analysis, offering the advantage of showing the exact place of production. All subjects from the GSTF and GTF that did not present distortions had better severity indexes compared to the subjects that presented it. The ACI index indicated that the GSTF without distortion had better performance, showing that it is adequate to measure articulatory competence.
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Pokorný, Vojtěch. "Systém pro odstranění vad řeči u dětí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316837.

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Speech is one of the basic forms of human communication and disruption of communication ability can negatively affect the life of man. deals with the elaboration of logopedic theory, which serves as a basis for the follow-up design and application creation in the field of speech therapy. The thesis is divided into five chapters. The first chapter deals with the history of speech therapy with an emphasis on domestic logopaedia, the logopedic intervention process in its entirety, and the differentiation of individual types of impaired communication skills. The second chapter focuses on a specific disruption of communication skills - dyslalia.The application for the treatment of dyslalia is determined by the application, which is elaborated in the third chapter. The fourth chapter contains a detailed description of the designed and created application, including an explanation of the processes running in the application when the application is being used. The last chapter of this diploma thesis contains a summary of the results of the practical testing of the application together with suggestions for improvement of the application.
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Sodré, Bruno Ribeiro. "Reconhecimento de padrões aplicados à identificação de patologias de laringe." Universidade Tecnológica Federal do Paraná, 2016. http://repositorio.utfpr.edu.br/jspui/handle/1/2013.

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As patologias que afetam a laringe estão aumentando consideravelmente nos últimos anos devido à condição da sociedade atual onde há hábitos não saudáveis como fumo, álcool e tabaco e um abuso vocal cada vez maior, talvez por conta do aumento da poluição sonora, principalmente nos grandes centros urbanos. Atualmente o exame utilizado pela endoscopia per-oral, direcionado a identificar patologias de laringe, são a videolaringoscopia e videoestroboscopia, ambos invasivos e por muitas vezes desconfortável ao paciente. Buscando melhorar o bem estar e minimizar o desconforto dos pacientes que necessitam submeter-se a estes procedimentos, este estudo tem como objetivo reconhecer padrões que possam ser aplicados à identificação de patologias de laringe de modo a auxiliar na criação de um novo método não invasivo em substituição ao método atual. Este trabalho utilizará várias configurações diferentes de redes neurais. A primeira rede neural foi gerada a partir de 524.287 resultados obtidos através das configurações k-k das 19 medidas acústicas disponíveis neste trabalho. Esta configuração atingiu uma acurácia de 99,5% (média de 96,99±2,08%) ao utilizar uma configuração com 11 e com 12 medidas acústicas dentre as 19 disponíveis. Utilizando-se 3 medidas rotacionadas (obtidas através do método de componentes principais), foi obtido uma acurácia de 93,98±0,24%. Com 6 medidas rotacionadas, o resultado obtido foi de acurácia foi de 94,07±0,29%. Para 6 medidas rotacionadas com entrada normalizada, a acurácia encontrada foi de 97,88±1,53%. A rede neural que fez 23 diferentes classificações, voz normal mais 22 patologias, mostrou que as melhores classificações, de acordo com a acurácia, são a da patologia hiperfunção com 58,23±18,98% e a voz normal com 52,15±18,31%. Já para a pior patologia a ser classificada, encontrou-se a fadiga vocal com 0,57±1,99%. Excluindo-se a voz normal, ou seja, utilizando uma rede neural composta somente por vozes patológicas, a hiperfunção continua sendo a mais facilmente identificável com uma acurácia de 57,3±19,55%, a segunda patologia mais facilmente identificável é a constrição ântero-posterior com 18,14±11,45%. Nesta configuração, a patologia mais difícil de se classificar continua sendo a fadiga vocal com 0,7±2,14%. A rede com re-amostragem obteve uma acurácia de 25,88±10,15% enquanto que a rede com re-amostragem e alteração de neurônios na camada intermediária obteve uma acurácia de 21,47±7,58% para 30 neurônios e uma acurácia de 18,44±6,57% para 40 neurônios. Por fim foi feita uma máquina de vetores suporte que encontrou um resultado de 67±6,2%. Assim, mostrou-se que as medidas acústicas precisam ser aprimoradas para a obtenção de melhores resultados de classificação dentre as patologias de laringe estudadas. Ainda assim, verificou-se que é possível discriminar locutores normais daqueles pacientes disfônicos.
Diseases that affect the larynx have been considerably increased in recent years due to the condition of nowadays society where there have been unhealthy habits like smoking, alcohol and tobacco and an increased vocal abuse, perhaps due to the increase in noise pollution, especially in large urban cities. Currently the exam performed by per-oral endoscopy (aimed to identify laryngeal pathologies) have been videolaryngoscopy and videostroboscopy, both invasive and often uncomfortable to the patient. Seeking to improve the comfort of the patients who need to undergo through these procedures, this study aims to identify acoustic patterns that can be applied to the identification of laryngeal pathologies in order to creating a new non-invasive larynx assessment method. Here two different configurations of neural networks were used. The first one was generated from 524.287 combinations of 19 acoustic measurements to classify voices into normal or from a diseased larynx, and achieved an max accuracy of 99.5% (96.99±2.08%). Using 3 and 6 rotated measurements (obtained from the principal components analysis method), the accuracy was 93.98±0.24% and 94.07±0.29%, respectively. With 6 rotated measurements from a previouly standardization of the 19 acoustic measurements, the accuracy was 97.88±1.53%. The second one, to classify 23 different voice types (including normal voices), showed better accuracy in identifying hiperfunctioned larynxes and normal voices, with 58.23±18.98% and 52.15±18.31%, respectively. The worst accuracy was obtained from vocal fatigues, with 0.57±1.99%. Excluding normal voices of the analysis, hyperfunctioned voices remained the most easily identifiable (with an accuracy of 57.3±19.55%) followed by anterior-posterior constriction (with 18.14±11.45%), and the most difficult condition to be identified remained vocal fatigue (with 0.7±2.14%). Re-sampling the neural networks input vectors, it was obtained accuracies of 25.88±10.15%, 21.47±7.58%, and 18.44±6.57% from such networks with 20, 30, and 40 hidden layer neurons, respectively. For comparison, classification using support vector machine produced an accuracy of 67±6.2%. Thus, it was shown that the acoustic measurements need to be improved to achieve better results of classification among the studied laryngeal pathologies. Even so, it was found that is possible to discriminate normal from dysphonic speakers.
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Vošická, Edita. "Autismus - Použití systémů alternativní a augmentativní komunikace u jedinců s poruchou autistického spektra." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-306901.

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This diploma thesis deals with alternative and augmentative communication systems using at the individuals with pervasive development disorders. Thesis is divided the theoretical and the experimental part. Theoretical part contains mainly informations about communication disability, speech development, diagnosis of communication abilities, alternative and augmentative communication systems and possibilities of aid to the families with child with pervasive development disorder. Experimental part presents research using questionnaire, which was send to the parents or legal representatives with child with pervasive development disorder. The research prepares base for next extensive researchs.
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Books on the topic "Automatic diagnosis of speech disorder"

1

Baghai-Ravary, Ladan. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders. New York, NY: Springer New York, 2013.

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Baghai-Ravary, Ladan, and Steve W. Beet. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4574-6.

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Dodd, Barbara. The differential diagnosis and treatment of children with speech disorder. London: Whurr Publishers, 1995.

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Dodd, Barbara. The differential diagnosis and treatment of children with speech disorder. London: Whurr, 1995.

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Managing organizational change: Human factors and automation. Philadelphia: Taylor & Francis, 1988.

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Learning disabilities sourcebook: Basic consumer health information about dyslexia, dyscalculia, dysgraphia, speech and communication disorders, auditory and visual processing disorders, and other conditions that make learning difficult, including attention deficit hyperactivity disorder, down syndrome and other chromosomal disorders, fetal alcohol spectrum disorders, hearing and visual impairment, autism and other pervasive developmental disorders, and traumatic brain Injury; along with facts about diagnosing learning disabilities, early intervention, the special education process, legal protections, assistive technology, and accommodations, and guidelines for life-stage transitions, suggestions for coping with daily challenges, a glossary of related terms, and a directory of additional resources. 4th ed. Detroit, MI: Omnigraphics, 2012.

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Baghai-Ravary, Ladan, and Steve W. Beet. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders. Springer, 2012.

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Baghai-Ravary, Ladan. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders. Springer, 2012.

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Dodd, Barbara. Differential Diagnosis of Children with Speech Disorder. Not Avail, 1994.

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Barbara, Dodd, ed. Differential diagnosis and treatment of children with speech disorder. 2nd ed. London: Whurr, 2005.

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Book chapters on the topic "Automatic diagnosis of speech disorder"

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Kaneri, Sushmita, Deepali Joshi, and Ranjana Jadhav. "Automatic Diagnosis of Attention Deficit/Hyperactivity Disorder." In Machine Learning and Information Processing, 127–35. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3_12.

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Jain, Deepti, Sandhya Arora, and C. K. Jha. "Diagnosis of Psychopathic Personality Disorder with Speech Patterns." In Communications in Computer and Information Science, 411–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0108-1_38.

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Yang, Zhixin, Hualiang Li, Li Li, Kai Zhang, Chaolin Xiong, and Yuzhong Liu. "Speech-Based Automatic Recognition Technology for Major Depression Disorder." In Human Centered Computing, 546–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37429-7_55.

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Dodd, Barbara, Tania Russell, and Michael Oerlemans. "Does a Past History of Speech Disorder Predict Literacy Difficulties?" In Reading Disabilities: Diagnosis and Component Processes, 199–212. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1988-7_9.

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Lopez-de-Ipiña, K., J. Solé-Casals, J. B. Alonso, C. M. Travieso, M. Ecay, and P. Martinez-Lage. "On the Alzheimer’s Disease Diagnosis: Automatic Spontaneous Speech Analysis." In Transactions on Computational Collective Intelligence XVII, 272–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44994-3_14.

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Pompili, Anna, Alberto Abad, Paolo Romano, Isabel P. Martins, Rita Cardoso, Helena Santos, Joana Carvalho, Isabel Guimarães, and Joaquim J. Ferreira. "Automatic Detection of Parkinson’s Disease: An Experimental Analysis of Common Speech Production Tasks Used for Diagnosis." In Text, Speech, and Dialogue, 411–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64206-2_46.

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Igual, Laura, Joan Carles Soliva, Roger Gimeno, Sergio Escalera, Oscar Vilarroya, and Petia Radeva. "Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention-Deficit/Hyperactivity Disorder." In Lecture Notes in Computer Science, 222–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31298-4_27.

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Mumtaz, Wajid, Lukáš Vařeka, and Roman Mouček. "Investigation of EEG-Based Graph-Theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder." In Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 205–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30493-5_23.

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López-de-Ipiña, Karmele, Jesús B. Alonso, Nora Barroso, Marcos Faundez-Zanuy, Miriam Ecay, Jordi Solé-Casals, Carlos M. Travieso, Ainara Estanga, and Aitzol Ezeiza. "New Approaches for Alzheimer’s Disease Diagnosis Based on Automatic Spontaneous Speech Analysis and Emotional Temperature." In Lecture Notes in Computer Science, 407–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35395-6_55.

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"The differential diagnosis of thought disorder." In Schizophrenic Speech, 48–79. Cambridge University Press, 2001. http://dx.doi.org/10.1017/cbo9780511544057.004.

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Conference papers on the topic "Automatic diagnosis of speech disorder"

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Hammami, Nacereddine, Mouldi Bedda, Nadir Farah, and Sihem Mansouri. "/r/-Letter disorder diagnosis (/r/-LDD): Arabic speech database development for automatic diagnosis of childhood speech disorders (Case study)." In 2015 Intelligent Systems and Computer Vision (ISCV). IEEE, 2015. http://dx.doi.org/10.1109/isacv.2015.7105542.

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Vyas, Garima, Malay Kishore Dutta, Jiri Prinosil, and Pavol Harar. "An automatic diagnosis and assessment of dysarthric speech using speech disorder specific prosodic features." In 2016 39th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2016. http://dx.doi.org/10.1109/tsp.2016.7760933.

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Albornoz, E. M., L. D. Vignolo, C. E. Martinez, and D. H. Milone. "Genetic wrapper approach for automatic diagnosis of speech disorders related to Autism." In 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2013. http://dx.doi.org/10.1109/cinti.2013.6705227.

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Srivastava, Neelesh, Mansi Bhatnagar, Anjali Yadav, Malay Kishore Dutta, and Carlos M. Travieso. "Machine learning based improved automatic diagnosis of cardiac disorder." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3309772.3309783.

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Ren, Zhao, Jing Han, Nicholas Cummins, Qiuqiang Kong, Mark D. Plumbley, and Björn W. Schuller. "Multi-instance Learning for Bipolar Disorder Diagnosis using Weakly Labelled Speech Data." In DPH2019: 9th International Digital Public Health Conference (2019). New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3357729.3357743.

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Ijitona, T. B., J. J. Soraghan, A. Lowit, G. Di-Caterina, and H. Yue. "Automatic Detection of Speech Disorder in Dysarthria using Extended Speech Feature Extraction and Neural Networks Classification." In IET 3rd International Conference on Intelligent Signal Processing (ISP 2017). Institution of Engineering and Technology, 2017. http://dx.doi.org/10.1049/cp.2017.0360.

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"Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis." In Special Session on Challenges in Neuroengineering. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004188606980705.

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Chen, Chin-Po, Xian-Hong Tseng, Susan Shur-Fen Gau, and Chi-Chun Lee. "Computing Multimodal Dyadic Behaviors During Spontaneous Diagnosis Interviews Toward Automatic Categorization of Autism Spectrum Disorder." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-563.

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Pahwa, Anjali, Gaurav Aggarwal, and Ashutosh Sharma. "A machine learning approach for identification & diagnosing features of Neurodevelopmental disorders using speech and spoken sentences." In 2016 International Conference on Computing, Communication and Automation (ICCCA). IEEE, 2016. http://dx.doi.org/10.1109/ccaa.2016.7813749.

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Padilla, Jennifer, Thierry Morlet, Kyoko Nagao, Rachel Crum, L. Ashleigh Greenwood, Jessica Loson, and Sarah Zavala. "Speech perception capabilities in children a few years after initial diagnosis of auditory processing disorder." In 169th Meeting of the Acoustical Society of America. Acoustical Society of America, 2016. http://dx.doi.org/10.1121/2.0000169.

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Reports on the topic "Automatic diagnosis of speech disorder"

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Cogswell, Pamela. A Study of the Association Among the Diagnosis of Speech-Language Impairments and the Diagnoses of Learning Disabilities and/or Attention Deficit Hyperactivity Disorder. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6104.

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