Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Electronic address: xuwh@pumch.cn.
Department of Radiology and Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea. kwak8140@jbnu.ac.kr.
Department of Radiology and Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea.
Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Anatomy and Neuroscience (JC), School of Biomedical Sciences, University of Melbourne, Parkville; Department of Surgery (JC), Alfred Hospital, Melbourne, Victoria; Interventional Radiology Service (JM, MB, HA), Department of Radiology, Austin Hospital, Melbourne; School of Medicine (JM, MB, HA), Faculty of Health, Deakin University, Waurn Ponds; Stroke Division (JM, MB, HA), Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria; Interventional Neuroradiology Service (HA), Department of Radiology, St Vincent's Hospital; Interventional Neuroradiology Unit (RVC, L-AS, HA), Monash Imaging, Monash Health; and Faculty of Medicine (RVC, HA), Nursing and Health Sciences, Monash University, Melbourne, Australia.
Anatomy and Neuroscience (JC), School of Biomedical Sciences, University of Melbourne, Parkville; Department of Surgery (JC), Alfred Hospital, Melbourne, Victoria; Interventional Radiology Service (JM, MB, HA), Department of Radiology, Austin Hospital, Melbourne; School of Medicine (JM, MB, HA), Faculty of Health, Deakin University, Waurn Ponds; Stroke Division (JM, MB, HA), Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria; Interventional Neuroradiology Service (HA), Department of Radiology, St Vincent's Hospital; Interventional Neuroradiology Unit (RVC, L-AS, HA), Monash Imaging, Monash Health; and Faculty of Medicine (RVC, HA), Nursing and Health Sciences, Monash University, Melbourne, Australia.
Transcribing disordered speech can be useful when diagnosing motor speech disorders such as primary progressive apraxia of speech (PPAOS), who have sound additions, deletions, and substitutions, or di...
Forty-five patients with PPAOS and 22 healthy controls were recorded repeating 13 words, 3 times each, which were transcribed manually and using wav2vec 2.0. The WER and phonetic and prosodic speech e...
Mean overall WER was 0.88 for patients and 0.33 for controls. WER significantly correlated with AOS severity and accurately distinguished between patients and controls but not between AOS subtypes. Th...
This study demonstrates that ASR can be useful in differentiating healthy from disordered speech and evaluating PPAOS severity but does not distinguish PPAOS subtypes. ASR transcriptions showed weak a...
https://doi.org/10.23641/asha.26359417....
Automatic Speech Recognition (ASR) technologies can be life-changing for individuals who suffer from dysarthria, a speech impairment that affects articulatory muscles and results in incomprehensive sp...
To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews....
The effects of the two feedback methods on student performance in medical interviews were compared using a prospective observational trial. Seventy-nine medical students in a clinical clerkship were a...
According to the study results, the mean diagnostic accuracy rate (SRS feedback group:1st mock 51.3%, 2nd mock 89.7%; IC recorder feedback group, 57.5%-67.5%; F(1, 77) = 4.0; p = 0.049), mini-CEX scor...
Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores....
This study was registered in the Japan Registry of Clinical Trials on June 14, 2022. Due to our misunderstanding of the trial registration requirements, we registered the trial retrospectively. This s...
Existing end-to-end speech recognition methods typically employ hybrid decoders based on CTC and Transformer. However, the issue of error accumulation in these hybrid decoders hinders further improvem...
In human-computer interaction systems, speech emotion recognition (SER) plays a crucial role because it enables computers to understand and react to users' emotions. In the past, SER has significantly...
Digital speech recognition is a challenging problem that requires the ability to learn complex signal characteristics such as frequency, pitch, intensity, timbre, and melody, which traditional methods...
Speech is a commonly used interaction-recognition technique in edutainment-based systems and is a key technology for smooth educational learning and user-system interaction. However, its application t...
Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medica...
Stroke patient data (n = 9049) from the years 2016-2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristi...
The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation...
An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevan...
This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122)....
The concentration of medical students in the classroom is important in promoting their mastery of knowledge. Multiple teaching characteristics, such as speaking speed, voice volume, and question use, ...
This research aims to analyze how teachers' linguistic characteristics affect medical students' classroom concentration based on a speech recognition toolkit and face recognition technology....
A speech recognition toolkit, WeNet, is used to recognize sentences during lectures in this study. Face recognition technology (FRT) is used to detect students' concentration in class. The study invol...
As a result of regression analysis, the explanatory power of the effect of the linguistic characteristics was 7.09% (F = 83.82, P < 0.001), with time, volume and question being significant influencing...
The results of this study support the significant positive influence of volume and questioning technique, the negative influence of time, and the insignificant influence of speaking speed and the inte...
Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models hav...
