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Jeong SM, Kim S, Lee EC, Kim HJ. Exploring Spectrogram-Based Audio Classification for Parkinson's Disease: A Study on Speech Classification and Qualitative Reliability Verification. SENSORS (BASEL, SWITZERLAND) 2024; 24:4625. [PMID: 39066023 PMCID: PMC11280556 DOI: 10.3390/s24144625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Patients suffering from Parkinson's disease suffer from voice impairment. In this study, we introduce models to classify normal and Parkinson's patients using their speech. We used an AST (audio spectrogram transformer), a transformer-based speech classification model that has recently outperformed CNN-based models in many fields, and a CNN-based PSLA (pretraining, sampling, labeling, and aggregation), a high-performance model in the existing speech classification field, for the study. This study compares and analyzes the models from both quantitative and qualitative perspectives. First, qualitatively, PSLA outperformed AST by more than 4% in accuracy, and the AUC was also higher, with 94.16% for AST and 97.43% for PSLA. Furthermore, we qualitatively evaluated the ability of the models to capture the acoustic features of Parkinson's through various CAM (class activation map)-based XAI (eXplainable AI) models such as GradCAM and EigenCAM. Based on PSLA, we found that the model focuses well on the muffled frequency band of Parkinson's speech, and the heatmap analysis of false positives and false negatives shows that the speech features are also visually represented when the model actually makes incorrect predictions. The contribution of this paper is that we not only found a suitable model for diagnosing Parkinson's through speech using two different types of models but also validated the predictions of the model in practice.
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Affiliation(s)
- Seung-Min Jeong
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-gil 20, Jongno-gu, Seoul 03016, Republic of Korea; (S.-M.J.); (S.K.)
| | - Seunghyun Kim
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-gil 20, Jongno-gu, Seoul 03016, Republic of Korea; (S.-M.J.); (S.K.)
| | - Eui Chul Lee
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Hongjimun 2-gil 20, Jongno-gu, Seoul 03016, Republic of Korea
| | - Han Joon Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Hospital, Daehak-ro 101, Jongno-gu, Seoul 03080, Republic of Korea
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Wang L, Qi C, Gu M, Yan M, Qi Q. Effects of stepped speech rehabilitation and psychological intervention on speech disorders and cognitive function in Parkinson disease patients. Medicine (Baltimore) 2023; 102:e36420. [PMID: 38206724 PMCID: PMC10754544 DOI: 10.1097/md.0000000000036420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/10/2023] [Indexed: 01/13/2024] Open
Abstract
To examine the impact of stepwise speech rehabilitation exercise therapy in the treatment of patients with Parkinson speech problems under psychological intervention on clinical results and cognitive functioning. Parkinson speech disorder patients who met the inclusion criteria were selected and divided into a control group and an observation group for training respectively. The control group used conventional nursing methods, including training in orofacial movement, vocalization, pitch, volume and breath control. The observation group used stepwise speech rehabilitation exercise intervention combined with psychotherapy nursing programme. In the statistical analysis, independent sample t-test and chi-square test were used to test the significance of the data processing methods. In the statistical analysis of baseline functional level (P > .05). The difference was not statistically significant. After 7 weeks of training, the mFDA level and speech intelligibility increased in both the observation and control groups. From the situation analysis of "modified drinking test" and the comparison of UPDRS-I scores, it can be seen that dysphagia and Parkinson dysphasia were reduced in both groups after training. The observation group spontaneous speech dimension was greater than the control group by around 0.07 in the aphasia comparison. Both groups displayed an upward trend in their MMSE and Montreal Cognitive Assessment (MoCA) when measuring cognitive function; the evaluation of P300, constructive function, and quality of life revealed this. The observation group P300 potential score was 0.13 points higher than that of the control group. The therapeutic training of stepped speech rehabilitation exercise care combined with psychological intervention has significant nursing effects on patients with Parkinson disease speech disorders, and the patients' cognitive functions have been effectively improved.
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Affiliation(s)
- Liping Wang
- Department of Neurology, Jinan City People’s Hospital, Jinan, 271199, Shandong, China
| | - Chengyan Qi
- Department of Neurology, Jinan City People’s Hospital, Jinan, 271199, Shandong, China
| | - Minmin Gu
- Department of Neurology, Jinan City People’s Hospital, Jinan, 271199, Shandong, China
| | - Min Yan
- Department of Neurology, Jinan City People’s Hospital, Jinan, 271199, Shandong, China
| | - Qinde Qi
- Department of Neurology, Jinan City People’s Hospital, Jinan, 271199, Shandong, China
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Johansson IL, Samuelsson C, Müller N. Consonant articulation acoustics and intelligibility in Swedish speakers with Parkinson's disease: a pilot study. CLINICAL LINGUISTICS & PHONETICS 2023; 37:845-865. [PMID: 35833475 DOI: 10.1080/02699206.2022.2095926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/16/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Imprecise consonant articulation is common in speakers with Parkinson's disease and can affect intelligibility. The research on the relationship between acoustic speech measures and intelligibility in Parkinson's disease is limited, and most of the research has been conducted on English. This pilot study investigated aspects of consonant articulation acoustics in eleven Swedish speakers with Parkinson's disease and six neurologically healthy persons. The focus of the study was on consonant cluster production, articulatory motion rate and variation, and voice onset time, and how these acoustic features correlate with speech intelligibility. Among the measures in the present study, typicality ratings of heterorganic consonant clusters /spr/ and /skr/ had the strongest correlations with intelligibility. Measures based on syllable repetition, such as repetition rate and voice onset time, showed varying results with weak to moderate correlations with intelligibility. One conclusion is that some acoustic measures may be more sensitive than others to the impact of the underlying sensory-motor impairment and dysarthria on speech production and intelligibility in speakers with Parkinson's disease. Some aspects of articulation appear to be equally demanding in terms of acoustic realisation for elderly healthy speakers and for speakers with Parkinson's disease, such as sequential motion rate measures. Clinically, this would imply that for the purpose of detecting signs of disordered speech motor control, choosing measures with less variation among older speakers without articulation impairment would lead to more robust results.
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Affiliation(s)
- Inga-Lena Johansson
- Department of Biomedical and Clinical Sciences/Speech and Language Pathology, Linköping University, Linköping, Sweden
| | - Christina Samuelsson
- Department of Biomedical and Clinical Sciences/Speech and Language Pathology, Linköping University, Linköping, Sweden
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Solna, Sweden
| | - Nicole Müller
- Department of Biomedical and Clinical Sciences/Speech and Language Pathology, Linköping University, Linköping, Sweden
- Department of Speech and Hearing Sciences, University College Cork, Cork, Ireland
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Kadambi P, Stegmann GM, Liss J, Berisha V, Hahn S. Wav2DDK: Analytical and Clinical Validation of an Automated Diadochokinetic Rate Estimation Algorithm on Remotely Collected Speech. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023; 66:3166-3181. [PMID: 37556308 PMCID: PMC10555468 DOI: 10.1044/2023_jslhr-22-00282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 11/08/2022] [Accepted: 06/05/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE Oral diadochokinesis is a useful task in assessment of speech motor function in the context of neurological disease. Remote collection of speech tasks provides a convenient alternative to in-clinic visits, but scoring these assessments can be a laborious process for clinicians. This work describes Wav2DDK, an automated algorithm for estimating the diadochokinetic (DDK) rate on remotely collected audio from healthy participants and participants with amyotrophic lateral sclerosis (ALS). METHOD Wav2DDK was developed using a corpus of 970 DDK assessments from healthy and ALS speakers where ground truth DDK rates were provided manually by trained annotators. The clinical utility of the algorithm was demonstrated on a corpus of 7,919 assessments collected longitudinally from 26 healthy controls and 82 ALS speakers. Corpora were collected via the participants' own mobile device, and instructions for speech elicitation were provided via a mobile app. DDK rate was estimated by parsing the character transcript from a deep neural network transformer acoustic model trained on healthy and ALS speech. RESULTS Algorithm estimated DDK rates are highly accurate, achieving .98 correlation with manual annotation, and an average error of only 0.071 syllables per second. The rate exactly matched ground truth for 83% of files and was within 0.5 syllables per second for 95% of files. Estimated rates achieve a high test-retest reliability (r = .95) and show good correlation with the revised ALS functional rating scale speech subscore (r = .67). CONCLUSION We demonstrate a system for automated DDK estimation that increases efficiency of calculation beyond manual annotation. Thorough analytical and clinical validation demonstrates that the algorithm is not only highly accurate, but also provides a convenient, clinically relevant metric for tracking longitudinal decline in ALS, serving to promote participation and diversity of participants in clinical research. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.23787033.
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Affiliation(s)
- Prad Kadambi
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe
- Aural Analytics Inc., Tempe, AZ
| | | | - Julie Liss
- School of Speech and Hearing Science, Arizona State University, Tempe
- Aural Analytics Inc., Tempe, AZ
| | - Visar Berisha
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe
- School of Speech and Hearing Science, Arizona State University, Tempe
- Aural Analytics Inc., Tempe, AZ
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Zhang J, Wu J, Qiu Y, Song A, Li W, Li X, Liu Y. Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review. Comput Biol Med 2023; 153:106517. [PMID: 36623438 PMCID: PMC9814440 DOI: 10.1016/j.compbiomed.2022.106517] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/23/2022] [Accepted: 12/31/2022] [Indexed: 01/07/2023]
Abstract
The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.
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Affiliation(s)
- Jun Zhang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China,Corresponding author
| | - Jingyue Wu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Yiyi Qiu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Aiguo Song
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Weifeng Li
- Department of Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Xin Li
- Department of Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yecheng Liu
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
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Ziegler W, Schölderle T, Brendel B, Risch V, Felber S, Ott K, Goldenberg G, Vogel M, Bötzel K, Zettl L, Lorenzl S, Lampe R, Strecker K, Synofzik M, Lindig T, Ackermann H, Staiger A. Speech and Nonspeech Parameters in the Clinical Assessment of Dysarthria: A Dimensional Analysis. Brain Sci 2023; 13:brainsci13010113. [PMID: 36672094 PMCID: PMC9856358 DOI: 10.3390/brainsci13010113] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 01/11/2023] Open
Abstract
Nonspeech (or paraspeech) parameters are widely used in clinical assessment of speech impairment in persons with dysarthria (PWD). Virtually every standard clinical instrument used in dysarthria diagnostics includes nonspeech parameters, often in considerable numbers. While theoretical considerations have challenged the validity of these measures as markers of speech impairment, only a few studies have directly examined their relationship to speech parameters on a broader scale. This study was designed to investigate how nonspeech parameters commonly used in clinical dysarthria assessment relate to speech characteristics of dysarthria in individuals with movement disorders. Maximum syllable repetition rates, accuracies, and rates of isolated and repetitive nonspeech oral-facial movements and maximum phonation times were compared with auditory-perceptual and acoustic speech parameters. Overall, 23 diagnostic parameters were assessed in a sample of 130 patients with movement disorders of six etiologies. Each variable was standardized for its distribution and for age and sex effects in 130 neurotypical speakers. Exploratory Graph Analysis (EGA) and Confirmatory Factor Analysis (CFA) were used to examine the factor structure underlying the diagnostic parameters. In the first analysis, we tested the hypothesis that nonspeech parameters combine with speech parameters within diagnostic dimensions representing domain-general motor control principles. In a second analysis, we tested the more specific hypotheses that diagnostic parameters split along effector (lip vs. tongue) or functional (speed vs. accuracy) rather than task boundaries. Our findings contradict the view that nonspeech parameters currently used in dysarthria diagnostics are congruent with diagnostic measures of speech characteristics in PWD.
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Affiliation(s)
- Wolfram Ziegler
- Clinical Neuropsychology Research Group (EKN), Institute of Phonetics and Speech Processing, Ludwig-Maximilians-University, 80799 Munich, Germany
- Correspondence:
| | - Theresa Schölderle
- Clinical Neuropsychology Research Group (EKN), Institute of Phonetics and Speech Processing, Ludwig-Maximilians-University, 80799 Munich, Germany
| | - Bettina Brendel
- Clinic for Psychiatry and Psychotherapy, Neurophysiology & Interventional Neuropsychiatry, University of Tübingen, 72076 Tübingen, Germany
| | - Verena Risch
- Clinical Neuropsychology Research Group (EKN), Institute of Phonetics and Speech Processing, Ludwig-Maximilians-University, 80799 Munich, Germany
| | - Stefanie Felber
- Clinical Neuropsychology Research Group (EKN), Institute of Phonetics and Speech Processing, Ludwig-Maximilians-University, 80799 Munich, Germany
| | - Katharina Ott
- Department of Neurology, Klinikum Großhadern, Ludwig-Maximilians-University, 81377 Munich, Germany
| | - Georg Goldenberg
- Clinic for Neuropsychology, City Hospital Munich Bogenhausen, 81925 Munich, Germany
| | - Mathias Vogel
- Clinic for Neuropsychology, City Hospital Munich Bogenhausen, 81925 Munich, Germany
| | - Kai Bötzel
- Department of Neurology, Klinikum Großhadern, Ludwig-Maximilians-University, 81377 Munich, Germany
| | - Lena Zettl
- Medical Clinic and Outpatient Clinic IV, Ludwig-Maximilians-University, 81377 Munich, Germany
| | - Stefan Lorenzl
- Clinic for Neurology, Hospital Agatharied, 83734 Hausham, Germany
| | - Renée Lampe
- School of Medicine, Klinikum Rechts der Isar, Orthopedic Department, Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Technical University of Munich, 81675 Munich, Germany
| | - Katrin Strecker
- Department of Logopedics, Stiftung ICP Munich, Center for Cerebral Palsy, 81377 Munich, Germany
| | - Matthis Synofzik
- Department of Neurodegenerative Disease, Hertie-Institute for Clinical Brain Research, German Center for Neurodegenerative Diseases (DZNE), and Center for Neurology, University of Tübingen, 72076 Tübingen, Germany
| | - Tobias Lindig
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, 72076 Tübingen, Germany
| | - Hermann Ackermann
- Department of General Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany
| | - Anja Staiger
- Clinical Neuropsychology Research Group (EKN), Institute of Phonetics and Speech Processing, Ludwig-Maximilians-University, 80799 Munich, Germany
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Patel S, Grabowski C, Dayalu V, Testa AJ. Speech error rates after a sports-related concussion. Front Psychol 2023; 14:1135441. [PMID: 36960009 PMCID: PMC10027790 DOI: 10.3389/fpsyg.2023.1135441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
Background Alterations in speech have long been identified as indicators of various neurologic conditions including traumatic brain injury, neurodegenerative diseases, and stroke. The extent to which speech errors occur in milder brain injuries, such as sports-related concussions, is unknown. The present study examined speech error rates in student athletes after a sports-related concussion compared to pre-injury speech performance in order to determine the presence and relevant characteristics of changes in speech production in this less easily detected neurologic condition. Methods A within-subjects pre/post-injury design was used. A total of 359 Division I student athletes participated in pre-season baseline speech testing. Of these, 27 athletes (18-22 years) who sustained a concussion also participated in speech testing in the days immediately following diagnosis of concussion. Picture description tasks were utilized to prompt connected speech samples. These samples were recorded and then transcribed for identification of errors and disfluencies. These were coded by two trained raters using a 6-category system that included 14 types of error metrics. Results Repeated measures analysis of variance was used to compare the difference in error rates at baseline and post-concussion. Results revealed significant increases in the speech error categories of pauses and time fillers (interjections/fillers). Additionally, regression analysis showed that a different pattern of errors and disfluencies occur after a sports-related concussion (primarily time fillers) compared to pre-injury (primarily pauses). Conclusion Results demonstrate that speech error rates increase following even mild head injuries, in particular, sports-related concussion. Furthermore, the speech error patterns driving this increase in speech errors, rate of pauses and interjections, are distinct features of this neurological injury, which is in contrast with more severe injuries that are marked by articulation errors and an overall reduction in verbal output. Future studies should consider speech as a diagnostic tool for concussion.
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Affiliation(s)
- Sona Patel
- Department of Speech-Language Pathology, Seton Hall University, Nutley, NJ, United States
- Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, NJ, United States
- *Correspondence: Sona Patel,
| | - Caryn Grabowski
- Department of Speech-Language Pathology, Seton Hall University, Nutley, NJ, United States
| | - Vikram Dayalu
- Department of Speech-Language Pathology, Seton Hall University, Nutley, NJ, United States
| | - Anthony J. Testa
- Center for Sports Medicine, Seton Hall University, South Orange, NJ, United States
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Ghane M, Ang MC, Nilashi M, Sorooshian S. Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Rueda A, Vásquez-Correa JC, Orozco-Arroyave JR, Nöth E, Krishnan S. Empirical Mode Decomposition articulation feature extraction on Parkinson’s Diadochokinesia. COMPUT SPEECH LANG 2022. [DOI: 10.1016/j.csl.2021.101322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Kent RD, Kim Y, Chen LM. Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2022; 65:574-623. [PMID: 34958599 DOI: 10.1044/2021_jslhr-21-00396] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
PURPOSE The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis. METHOD Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate. RESULTS Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions. DISCUSSION A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.
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Affiliation(s)
- Ray D Kent
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison
| | - Yunjung Kim
- School of Communication Sciences & Disorders, Florida State University, Tallahassee
| | - Li-Mei Chen
- Department of Foreign Languages and Literature, National Cheng Kung University, Tainan, Taiwan
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11
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On the Primary Influences of Age on Articulation and Phonation in Maximum Performance Tasks. LANGUAGES 2021. [DOI: 10.3390/languages6040174] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Maximum performance tasks have been identified as possible domains where incipient signs of neurological disease may be detected in simple speech and voice samples. However, it is likely that these will simultaneously be influenced by the age and sex of the speaker. In this study, a comprehensive set of acoustic quantifications were collected from the literature and applied to productions of sustained [a] productions and Alternating Motion Rate diadochokinetic (DDK) syllable sequences made by 130 (62 women, 68 men) healthy speakers, aged 20–90 years. The participants were asked to produce as stable (sustained [a] and DDK) and fast (DDK) productions as possible. The full set of features were reduced to a functional subset that most efficiently modeled sex-specific differences between younger and older speakers using a cross-validation procedure. Twelve measures of [a] and 16 measures of DDK sequences were identified across men and women and investigated in terms of how they were altered with increasing age of speakers. Increased production instability is observed in both tasks, primarily above the age of 60 years. DDK sequences were slower in older speakers, but also altered in their syllable and segment level acoustic properties. Increasing age does not appear to affect phonation or articulation uniformly, and men and women are affected differently in most quantifications investigated.
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12
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An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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13
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Corcoran CM, Mittal VA, Bearden CE, E Gur R, Hitczenko K, Bilgrami Z, Savic A, Cecchi GA, Wolff P. Language as a biomarker for psychosis: A natural language processing approach. Schizophr Res 2020; 226:158-166. [PMID: 32499162 PMCID: PMC7704556 DOI: 10.1016/j.schres.2020.04.032] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 12/21/2022]
Abstract
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.
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Affiliation(s)
- Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, CA, USA; Department of Psychology, Semel Institute for Neuroscience and Human Behavior, Brain Research Institute, University of California Los Angeles, CA, USA; Department of Psychology, University of California Los Angeles, CA USA
| | - Raquel E Gur
- Brain Behavior Laboratory, Neuropsychiatry Division, Department of Psychiatry, Philadelphia, PA 19104, USA
| | - Kasia Hitczenko
- Department of Linguistics, Northwestern University, Evanston, IL, USA
| | - Zarina Bilgrami
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aleksandar Savic
- Department of Diagnostics and Intensive Care, University Psychiatric Hospital Vrapce, Zagreb, Croatia
| | - Guillermo A Cecchi
- Computational Biology Center-Neuroscience, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA, USA.
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