Short Words Research Articles

Artificial Intelligence-Generated Writing in the ERAS Personal Statement: An Emerging Quandary for Post-graduate Medical Education.

This study was designed to investigate if artificial intelligence (AI) detection software can determine the use of AI in personal statements for residency applications. Previously written personal statements were collected from physicians who had already matched to residency through the Electronic Residency Application System. Physicians were recruited for the study through collegial relationships and were given study information via email. The study team constructed five parallel statements from the shared personal statements to prompt AI to create a personal statement of similar content. An online AI detection tool, GPTZero, was used to assess all the personal statements. Statistical analyses were conducted using R. Descriptive statistics, t-tests, and Pearson correlations were used to assess the data. Eight physicians' statements were compared to eight AI-generated statements. GPTZero was able to correctly identify AI-generated writing, assigning them significantly higher AI probability scores compared to human-authored essays. Human-generated statements were considered more readable, used shorter words with fewer syllables, and had more sentences compared to AI-generated essays. Longer average sentence length, low readability scores, and high SAT word percentages were strongly associated with AI-generated essays. This study shows the capacity of GPTZero to distinguish human-created versus AI-generated writing. Use of AI can pose significant ethical challenges and carries a risk of inadvertent harm to certain applicants and erosion of trust in the application process. Authors suggest standardization of protocol regarding the use of AI prior to its integration in post-graduate medical education.

Digital speech hearing screening using a quick novel mobile hearing impairment assessment: an observational correlation study

By 2050, 1 in 4 people worldwide will be living with hearing impairment. We propose a digital Speech Hearing Screener (dSHS) using short nonsense word recognition to measure speech-hearing ability. The importance of hearing screening is increasing due to the anticipated increase in individuals with hearing impairment globally. We compare dSHS outcomes with standardized pure-tone averages (PTA) and speech-recognition thresholds (SRT). Fifty participants (aged 55 or older underwent pure-tone and speech-recognition thresholding. One-way ANOVA was used to compare differences between hearing impaired and hearing not-impaired groups, by the dSHS, with a clinical threshold of moderately impaired hearing at 35 dB and severe hearing impairment at 50 dB. dSHS results significantly correlated with PTAs/SRTs. ANOVA results revealed the dSHS was significantly different (F(1,47) = 38.1, p < 0.001) between hearing impaired and unimpaired groups. Classification analysis using a 35 dB threshold, yielded accuracy of 85.7% for PTA-based impairment and 81.6% for SRT-based impairment. At a 50 dB threshold, dSHS classification accuracy was 79.6% for PTA-based impairment (Negative Predictive Value (NPV)-93%) and 83.7% (NPV-100%) for SRT-based impairment. The dSHS successfully differentiates between hearing-impaired and unimpaired individuals in under 3 min. This hearing screener offers a time-saving, in-clinic hearing screening to streamline the triage of those with likely hearing impairment to the appropriate follow-up assessment, thereby improving the quality of services. Future work will investigate the ability of the dSHS to help rule out hearing impairment as a cause or confounder in clinical and research applications.

Therapeutic Cerebral Fluid Puncture in Patients with Idiopathic Intracranial Hypertension: No Short-Term Effect on Neurocognitive Function.

Idiopathic intracranial hypertension (IIH) is typically characterized by headaches and vision loss. However, neurocognitive deficits are also described. Our study aimed to test the influence of therapeutic lumbar puncture on the latter. A total of 15 patients with IIH were tested with a battery of neurocognitive tests at baseline and after therapeutic lumbar drainage. Hereby, Logical Memory of the Wechsler Memory Scale-Revised Edition (WMS-R), the California Verbal Learning Test Short Version (CVLT), alertness, selective attention, and word fluency were used. Changes in cognitive functioning in the course of CSF pressure lowering were analysed and compared with age, sex, and education-matched healthy controls. Before intervention, scores of Logical Memory, the RWT, and the HADS-D were significantly lower in IIH patients compared to matched controls. After short-term normalization of CSF pressure, the RWT improved significantly. Additionally, significant positive correlations were found between headache intensity and subjective impairment, as well as between BMI and CSF opening pressure. Our findings confirm lower performance in terms of long-term verbal memory and word fluency compared to controls, as well as depressive symptoms in IIH patients. Significant improvement after short-term normalization of intracranial pressure by means of CSF drainage was seen only for word fluency. This indicates that short-term normalization of CSF pressure is not sufficient to normalize observed neurocognitive deficits.

CNN Based Automatic Speech Recognition: A Comparative Study

Recently, one of the most common approaches used in speech recognition is deep learning. The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). Since CNNs can capture local features effectively, they are applied to tasks with relatively short-term dependencies, such as keyword detection or phoneme- level sequence recognition. This paper presents the development of a deep learning and speech command recognition system. The Google Speech Commands Dataset has been used for training. The dataset contained 65.000 one-second-long words of 30 short English words. That is, %80 of the dataset has been used in the training and %20 of the dataset has been used in the testing. The data set consists of one-second voice commands that have been converted into a spectrogram and used to train different artificial neural network (ANN) models. Various variants of CNN are used in deep learning applications. The performance of the proposed model has reached %94.60.

Targeting Phonology or Semantics to Improve Reading Aloud Response Times and Accuracy: A Case Series Investigation of Stroke Survivors With Aphasia.

Acquired reading deficits, or alexia, affect a significant proportion of individuals with aphasia. We sought to improve treatment for alexia by targeting specific cognitive information-processing components critical to reading (i.e., phonology or semantics). To target either phonological or semantic processing, we administered two anomia treatments, phonomotor treatment (PMT) and semantic feature analysis, modified to include a focus on reading throughout the therapy. Chronic left-hemisphere stroke survivors (N = 5) completed one or two 60-hr treatment rounds. Based on predictions from a computational reading model, three participants received the treatment recommended for their specific reading challenges (e.g., PMT for phonological deficits), while two participants had the nonrecommended treatment first, followed by the recommended model-matched treatment. Changes in reading aloud accuracy and response times (RTs) from before to after treatment were examined as a function of matching treatment to the deficit profile, type of treatment, therapy round, and word characteristics. Participants' reading aloud accuracy improved after treatment relative to baseline with higher accuracy for high-frequency words and shorter words. After the first treatment round, participants' accuracy and RT improved, irrespective of whether treatment was matched to the deficit profile. Furthermore, participants who completed the second treatment round continued achieving accuracy gains. Following treatment, participants demonstrated enhanced reading efficiency and generalized improvements on the selected sections of the Woodcock Reading Mastery Test. While larger studies are needed to test for the effects of matching treatment type to the deficit profile, we conclude that treatments targeting specific information-processing components can effectively improve reading. Doubling the treatment dose offers small but significant gains. https://doi.org/10.23641/asha.26517319.

The Enduring Challenge of Literacy Issues in Adulthood: Investigating Spelling Deficits among Dyslexic Italian University Students.

The issue of literacy challenges among dyslexic adults remains a significant concern. This study investigates spelling deficits among highly educated adults with dyslexia learning a transparent orthography. Thirty-eight Italian dyslexic university students were examined and compared to a group of age- and education-matched typical readers. Firstly, we analyzed spelling performance using a Passage Dictation Test. Additionally, lists of words varying in length and word frequency were dictated under two experimental conditions: a normal condition (NC) and an articulatory suppression condition (ASC). The ASC assessed the participants' ability to spell with interference to the phonological (sublexical) spelling procedure, i.e., the most likely compensated spelling strategy of Italian dyslexic spellers. The results clearly indicated that, in spelling the meaningful passage, dyslexic participants underperformed compared to the controls, with a prevalence of lexical errors, despite the comparison with the normative reference data showing only mild spelling difficulties. In spelling isolated words in normal conditions, dyslexic participants performed within the reference norms and as accurately as control participants across all stimuli (short words, high- and low-frequency words), except for long words, where their spelling difficulties were evident. Articulatory suppression significantly impaired dyslexics' performance on short stimuli, reducing the usual sublexical advantage associated with them, and exacerbated misspellings on long words. Additionally, articulatory suppression disproportionately affected dyslexics' performance on high-frequency words, diminishing the typical lexical advantage associated with these words. Results are discussed in terms of their theoretical, clinical, and educational implications.

Review on Sanskrit Sandhi Splitting using Deep Learning Techniques

A procedure called sandhi is used in Sanskrit to join short words (morphemes) to create compound words. A composite word are broken down into their component morphemes by a process known as sandhi splitting. This study focuses on several performance technologies and methodologies used to perform the above operation on Sanskrit sentences. Various approaches were identified for the problem from the literature survey. Initial approaches involved use of Finite State Transducers. Earlier the approaches introduced to increase accuracy include use of mathematical models and various optimality theories. Graph based approaches and parser based techniques were introduced later. With the advancement of deep learning techniques Recurrent Neural Networks, Long-Short Term Memory models and Double decoder models were adopted which involved training machine learning models through neural networks and classifier algorithms. Bidirectional LSTM models with attention mechanism, transformer based models and large language models like BERT were the most recent methodologies adopted and proved to be of higher accuracy and performance.

Translation of Network Catchwords from the Perspective of Skopos Theory

In today's age of social media, network catchwords play an important role of cultural communication and social symbols, expressing deep semantic concepts in short words and sentences, forming a fashionable and trendy oral communication mode. Its translation involves many aspects such as language, culture and social context. This paper will take the network catchwords in 2023 as an example, and from the perspective of skopos theory, exploring the meaning, usage context and cultural and social connotation of typical catchwords such as “new track”, “Chinese modernization”, “Ceiling” and “Ice-cream assassin”. A comprehensive exploration of how multiple meanings are handled in cross-cultural translation and how these meanings are maintained or transformed in different contexts. It will help us to have a deeper understanding of the importance of network buzzwords in contemporary cultural exchanges, and provide new inspiration and direction for future research in related fields.

Hybrid Multi Kernel Polar Code Construction and Optimized Decoding Algorithm for 5G Mobile Communications

5G mobile communications faces difficulty in channel coding to satisfy user requirements. This paper evaluates flexibility, reliability and complexity for different channel coding schemes(Turbo codes, Low Density Parity check codes, mother polar codes, Multi Kernel-polar codes) on regular channel AWGN with BPSK modulation of code rate R=1/2. The proposed hybrid Multi Kernel-polar codes will have greater flexibility and reliability for short and large code words. The optimized decoding of Multi Kernel polar codes reduces the number of computations by using repeated LLR’s of sub-kernel matrices.

Regular exposure facilitates dual-mapping of Cantonese pronunciation variants

Abstract The multilingual landscape of Canada creates opportunities for many heterogeneous bilingual communities to experience systematic phonetic variation within and across languages and dialects, and exposes listeners to different pronunciation variants. This paper examines phonetic variation through the lens of an ongoing sound change in Cantonese involving word-initial [n] and [l] across two primed lexical decision tasks (Experiment 1: Immediate repetition priming task, Experiment 2: Long-distance repetition priming task). Our main question is: How are sound change pronunciation variants recognized and represented in a Cantonese-English bilingual lexicon? The results of both experiments suggest that [n]- and [l]-initial variants facilitate processing in both short and long-term spoken word recognition. Thus, regular exposure to Cantonese endows bilingual listeners with the perceptual flexibility to dually and gradiently map pronunciation variants to a single lexical representation.

The Voice of the People: Populism and Donald Trump’s Use of Informal Voice

Many studies have examined characteristic verbal aspects of Donald J. Trump’s political communication, from his authoritarian rhetoric to his preference for short words and simple sentences, as expressions of his populism. This article focuses on his use of non-verbal voice quality. In analyzing the “Trump rallies” and other materials from his successful campaigning before the 2016 United States presidential election, I argue that Trump’s evocative and meaningful uses of pitch, amplitude, speech rate, rhythm, and other vocal measures combine to make his paralanguage exceptionally and counter-normatively informal, and that this informality amplifies his explicitly populist messaging. I conclude by suggesting that Trump’s informal voice solves an important problem for him: It allows him to express his populism with a deeply personal undertone, and thereby potentially to make his claims to popular identification ring intuitively true.

Study to evaluate the readability and visual appearance of online resources for blunt chest trauma: an evaluation of online resources using mixed methods

ObjectivesBlunt chest trauma (BCT) is characterised by forceful and non-penetrative impact to the chest region. Increased access to the internet has led to online healthcare resources becoming used by the...

Reading Easy Language texts written by public authorities

Previous research has shown that word length, frequency and word repetition influence word reading times (Rayner 1998; 2009). Guidelines for Easy Language advise writers to use frequent and short words, and to repeat words instead of using synonyms. However, some of these guidelines are based on research that has been misinterpreted, simplified, or is outdated (Wengelin 2015), and studies focusing on effects of word length, frequency and word repetition among adult readers in the Easy Swedish target group are lacking. This eye-tracking study investigated the reading of Easy Language texts written by public authorities, as well as the effects of word length, frequency, and word repetition on readers in a day centre for people with intellectual disabilities. The results showed significant effects for word length and frequency in all readers. In addition, the effects were significantly greater in the target group than in the control group. The effects for word repetition were not as clear, affecting only one of the reading measures. Furthermore, the study revealed poor comprehension rates in the target group, i.e., when asked, they were not able to reproduce the main contents of the texts. The significantly greater effects of word length and frequency suggest that the related Easy Language guidelines are valid for this group of readers. The poor comprehension rates indicate that the texts were too difficult for these readers.

Named Entity Recognition of an Oversampled and Preprocessed Manufacturing Data Corpus

In recent manufacturing industry, improving the manufacturing process is of paramount importance. One area that holds great potential for enhancement is the application and manipulation of maintenance data. By effectively leveraging this data, manufacturers can optimize maintenance schedules, leading to increased efficiency, reduced costs, and minimized downtime. However, the challenge lies in handling vast amounts of maintenance data that often come in various formats, making it difficult to extract valuable insights. Without proper analysis, this unprocessed data can result in unforeseen issues, costly disruptions, and extended downtime periods. To overcome this obstacle, modern manufacturing companies are turning to advanced technologies such as language modelling, text classification, machine translation, and Named Entity Recognition (NER). To the best of our knowledge, no investigation has been conducted to assess the impact of text preprocessing on NER performance. Improving the initial stage of NER, such as text preprocessing, can enhance NER performance which leads to the training model’s efficiency performance. In this study, Hidden Markov Model (HMM) is employed to improve NER performance by utilizing oversampling and text preprocessing techniques. The study is performed without IOB labelling and consider seven specific entities and the preprocessing text tasks include tokenization, lemmatization, erase punctuation, stop words removal, and elimination of long and short words. As a result, HMM for NER with oversampling and preprocessed text outperformed the one without any of both by 20.10% and 27.59%, respectively, due to consideration of significant classes and words among the entity classes in preprocessed factory reports. This finding highlights the importance of text preprocessing method selection in NER and its capability to optimize maintenance schedule and reduce downtime.

Scalable Data Concentrator with Baseline Interconnection Network for Triggerless Data Acquisition Systems

Triggerless data acquisition systems (DAQs) require that the data stream is transmitted from multiple links and into the processing node. The short input data words must be concentrated and packed into the longer bit vectors the output interface (e.g., PCI Express) uses. In that process, the unneeded data must be eliminated, and a dense stream of useful DAQ data must be created. Additionally, the time order of the data should be preserved. This paper presents a new solution using the Baseline Network with Reversed Outputs (BNRO) for high-speed data routing. A thorough analysis of the network’s operation enabled increased scalability compared to the previously published concentrator based on an 8 × 8 network. The solution may be scaled by adding additional layers to the BNRO network while minimizing resource consumption. Simulations were performed for four and five layers (16 and 32 inputs). The FPGA implementation and tests in the actual hardware were successfully performed for 16 inputs. The pipeline registers may be added in each layer independently, shortening the critical path and increasing the maximum acceptable clock frequency.

DO OLDER ADULTS APPRAISE ELDERSPEAK COMMUNICATION AS PATRONIZING OR RESPECTFUL IN HOSPITAL DEMENTIA CARE?

Abstract Elderspeak communication is speech that sounds like babytalk to older adults. The Iowa Coding Scheme for Elderspeak (ICodE) contains 11 elderspeak attributes: diminutives, childish terms, collectives, short words, directives, reflectives, minimizers, exaggerated praise, laughter, tag questions, and exaggerated prosody. Thirty-one older adults (aged 74.5±7.8 years) listened to 41 clips of nursing staff providing care to hospitalized patients with dementia and rated each clip using the 5-point Emotional Tone Rating Scale for patronizing and respectful (5=very, 1=not at all). Each clip was classified using the ICodE as either neutral (no elderspeak), containing a single attribute, or containing an attribute with exaggerated prosody. Linear mixed modeling was used to evaluate the differences in patronizing and respectful ratings between neutral clips and each attribute, and clips with each attribute and exaggerated prosody, while controlling for within-subjects correlation and adjusting for multiple comparisons. Mean ratings for neutral clips were 4.0±1.1 for respectful and 1.7±1.0 for patronizing. Ratings for elderspeak attributes ranged from 2.1 to 2.9 for patronizing and 2.8 to 3.8 for respectful. Ten of the 11 attributes, excluding exaggerated praise, were rated as significantly more patronizing and less respectful than neutral. The addition of exaggerated prosody was significant in comparison with the elderspeak attribute alone for exaggerated praise (more patronizing and less respectful), laughter (more patronizing), short words (less patronizing, more respectful), and diminutives (less respectful). This analysis validates that the attributes of the ICodE are considered more patronizing and less respectful by older adults compared to neutral communication in hospital dementia care.

ARE OLDER ADULTS’ PERCEPTIONS OF ELDERSPEAK INFLUENCED BY SELF-REPORTED PSYCHOSOCIAL CHARACTERISTICS?

Abstract PROMIS (Patient-Reported Outcomes Measurement Information System) is a system of highly reliable measures of patient-reported health status. Elderspeak is speech that sounds childish to older adults. Use of elderspeak by nursing staff can result in rejection of care. The Iowa Coding Scheme for Elderspeak (ICodE) contains 11 elderspeak attributes: diminutives, childish terms, collectives, short words, directives, reflectives, minimizers, exaggerated praise, laughter, tag questions and exaggerated prosody. Thirty-one older adults (aged 74.5±7.8 years) responded to short form PROMIS instruments measuring self-efficacy, physical function, isolation, cognitive function, depression, anxiety, and pain interference. Participants listened to 41 audio recordings of nursing staff providing care to hospitalized patients with dementia and were asked to rate each audio recording using a 5-point Emotional Tone Rating Scale for patronizing and respectful (5=very, 1=not at all). Using the ICodE, each audio recording was classified as neutral (no elderspeak), containing a single attribute, or containing an attribute combined with exaggerated prosody. Ratings for elderspeak attributes ranged from 2.1 to 2.9 for patronizing and 2.8 to 3.8 for respectful. Using the SPSS statistical package, a Spearman’s rho correlation was calculated for each elderspeak rating and health status domain identified above. Among this small, homogenous sample, no statistically significant correlation was found between participants’ self-efficacy, isolation, physical or cognitive function, depression, anxiety, or pain interference, as measured by PROMIS tools, and their rating of elderspeak attributes as respectful or patronizing. Future research should continue to explore what factors influence older adults’ perceptions of elderspeak as being respectful or patronizing.

The role of imageability in noun and verb acquisition in children with Down syndrome and their peers with typical development.

Our main objective was to analyze the role of imageability in relation to the age of acquisition (AoA) of nouns and verbs in Spanish-speaking children with Down syndrome (DS) and their peers with typical development (TD). The AoA of nouns and verbs was determined using the MacArthur-Bates CDIs adapted to the profile of children with DS. The AoA was analyzed using a linear mixed-effect model, including factors of imageability, group, and word class, and controlling for word frequency and word length. This analysis showed that high imaginable and short words were acquired early. Children with DS acquired the words later than TD peers. An interaction between imageability and group indicated that the effect of imageability was greater in the DS group. We discuss this effect considering DS children's phonological memory difficulties. The overall results confirm the role that imageability and word length play in lexical acquisition, an effect that goes beyond word class.

Clinical Manifestations.

The detection and characterization of speech changes may help in the identification of neurodegenerative diseases and have the potential to help with patient characterization and monitoring. Yet, there is limited research validating the presence of speech changes across different types of neurodegenerative disease. We report on the relationships between speech and other clinical assessments in the individuals with different dementia diagnoses and in comparison to healthy older adults. We analyzed speech recordings from 109 patients (52F, 57M; Age = 72.63± 8.61) who were diagnosed with various neurodegenerative diseases, including Alzheimer's disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 acoustic and linguistic features. We investigated the linear relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Western Aphasia Battery (WAB), and Mattis Dementia Rating Scale while controlling for age, sex and years of education. Speech features that were significantly associated with clinical measures were then included in group comparisons with healthy older adults (N = 74, ∼39F; Age = 61.31±7.29). Speech features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE scores were associated with the use of more familiar nouns (β = -1.60, p<.001). Similarly, increased impairment as assessed by the WAB was correlated with the use of higher frequency nouns (β = -0.01, p<.001). Patients used significantly more nouns (z = 6.25, p<.001) and shorter words (z = 8.33, p<.001) than the healthy older adults. Their speech duration was also significantly shorter (z = 7.98, p<.001) and they paused more (z = 5.19, p<.001). Speech changes representing decreased speech, with simpler vocabularies and syntax, were detectable in patients with different neurodegenerative diseases and correlated with clinical outcomes. These same speech patterns differed in patients with neurodegenerative disease compared to healthy older adults. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.

EEG-Based Classification of Spoken Words Using Machine Learning Approaches

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that affects the nerve cells in the brain and spinal cord. This condition leads to the loss of motor skills and, in many cases, the inability to speak. Decoding spoken words from electroencephalography (EEG) signals emerges as an essential tool to enhance the quality of life for these patients. This study compares two classification techniques: (1) the extraction of spectral power features across various frequency bands combined with support vector machines (PSD + SVM) and (2) EEGNet, a convolutional neural network specifically designed for EEG-based brain–computer interfaces. An EEG dataset was acquired from 32 electrodes in 28 healthy participants pronouncing five words in Spanish. Average accuracy rates of 91.04 ± 5.82% for Attention vs. Pronunciation, 73.91 ± 10.04% for Short words vs. Long words, 81.23 ± 10.47% for Word vs. Word, and 54.87 ± 14.51% in the multiclass scenario (All words) were achieved. EEGNet outperformed the PSD + SVM method in three of the four classification scenarios. These findings demonstrate the potential of EEGNet for decoding words from EEG signals, laying the groundwork for future research in ALS patients using non-invasive methods.