Linguistic Parameters of Spontaneous Speech for identifying Mild Cognitive Impairment and Alzheimer’s Disease

In this paper, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer’s Disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients and mAD patients, we will carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the paper, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we will analyze the efficacy of each feature group separately. Our model which uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68–70%, 2-class accuracy values: 77.3–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.

Automated Scoring of Tablet-Administered Expressive Language Tests

Speech and language impairments are common pediatric conditions, with as many as 10% of children experiencing one or both at some point during development. Expressive language disorders in particular often go undiagnosed, underscoring the immediate need for assessments of expressive language that can be administered and scored reliably and objectively. In this paper, we present a set of highly accurate computational models for automatically scoring several common expressive language tasks. In our assessment framework, instructions and stimuli are presented to the child on a tablet computer, which records the child's responses in real time, while a clinician controls the pace and presentation of the tasks using a second tablet. The recorded responses for four distinct expressive language tasks (expressive vocabulary, word structure, recalling sentences, and formulated sentences) are then scored using traditional paper-and-pencil scoring and using machine learning methods relying on a deep neural network-based language representation model. All four tasks can be scored automatically from both clean and verbatim speech transcripts with very high accuracy at the item level (83−99%). In addition, these automated scores correlate strongly and significantly (ρ = 0.76–0.99, p < 0.001) with manual item-level, raw, and scaled scores. These results point to the utility and potential of automated computationally-driven methods of both administering and scoring expressive language tasks for pediatric developmental language evaluation.

Leadership Sentiment and Price Fluctuations

The relationship between prices and leadership mood was examined using the example of crude oil prices and the sentiment of the CEOs of 5 leading oil production companies between 2014 and 2019. The crude oil market was chosen due to recent price fluctuations and upcoming challenges, and for its significant for the global economy. This study uses an empirical approach based on a mood analysis of the CEO's natural language. 160 speech transcripts were analyzed using a leading aspect-based sentiment analysis machine learning algorithm to obtain sentiment data. The relation between sentiment and oil price was tested using linear regression. The results of this study show in detail that the sentiment correlates positively in times of low prices and negatively in times of high prices. The average threshold price calculated using this method was 63 USD per barrel of West Texas Intermediate (WTI) crude oil in the observed period. This corresponds to analysts who estimated the ideal oil price for 2018 at USD 60 to 70. Finally, the restrictions and prospects are discussed. Findings of this study could aid investors decision making and advance the use of sentiment analysis in economic sciences.

Extending automatic transcripts in a unified data representation towards a prosodic-based metadata annotation and evaluation

This paper describes a framework that extends automatic speech transcripts in order to accommodate relevant information coming from manual transcripts, the speech signal itself, and other resources, like lexica. The proposed framework automatically collects, relates, computes, and stores all relevant information together in a self-contained data source, making it possible to easily provide a wide range of interconnected information suitable for speech analysis, training, and evaluating a number of automatic speech processing tasks. The main goal of this framework is to integrate different linguistic and paralinguistic layers of knowledge for a more complete view of their representation and interactions in several domains and languages. The processing chain is composed of two main stages, where the first consists of integrating the relevant manual annotations in the speech recognition data, and the second consists of further enriching the previous output in order to accommodate prosodic information. The described framework has been used for the identification and analysis of structural metadata in automatic speech transcripts. Initially put to use for automatic detection of punctuation marks and for capitalization recovery from speech data, it has also been recently used for studying the characterization of disfluencies in speech. It was already applied to several domains of Portuguese corpora, and also to English and Spanish Broadcast News corpora.

Impact of Trump's Promotion of Unproven COVID-19 Treatments on Social Media and Subsequent Internet Trends: Observational Study

Background Individuals with large followings can influence public opinions and behaviors, especially during a pandemic. In the early days of the pandemic, US president Donald J Trump has endorsed the use of unproven therapies. Subsequently, a death attributed to the wrongful ingestion of a chloroquine-containing compound occurred. Objective We investigated Donald J Trump’s speeches and Twitter posts, as well as Google searches and Amazon purchases, and television airtime for mentions of hydroxychloroquine, chloroquine, azithromycin, and remdesivir. Methods Twitter sourcing was catalogued with Factba.se, and analytics data, both past and present, were analyzed with Tweet Binder to assess average analytics data on key metrics. Donald J Trump’s time spent discussing unverified treatments on the United States’ 5 largest TV stations was catalogued with the Global Database of Events, Language, and Tone, and his speech transcripts were obtained from White House briefings. Google searches and shopping trends were analyzed with Google Trends. Amazon purchases were assessed using Helium 10 software. Results From March 1 to April 30, 2020, Donald J Trump made 11 tweets about unproven therapies and mentioned these therapies 65 times in White House briefings, especially touting hydroxychloroquine and chloroquine. These tweets had an impression reach of 300% above Donald J Trump’s average. Following these tweets, at least 2% of airtime on conservative networks for treatment modalities like azithromycin and continuous mentions of such treatments were observed on stations like Fox News. Google searches and purchases increased following his first press conference on March 19, 2020, and increased again following his tweets on March 21, 2020. The same is true for medications on Amazon, with purchases for medicine substitutes, such as hydroxychloroquine, increasing by 200%. Conclusions Individuals in positions of power can sway public purchasing, resulting in undesired effects when the individuals’ claims are unverified. Public health officials must work to dissuade the use of unproven treatments for COVID-19.

The conceptualization of morality in modern english-language globalization discourse: evidence from the United Nations Speeches

The increased interest of international cognitive linguists in the mechanisms of conceptualizing modern social phenomena has necessitated cognitive linguistic analysis of such phenomena as globalization, which is one of the most important trends setting the vector for modern society development.This study attempts not only to identify key concepts and means of their representation in terms of the globalization phenomenon, but also to build with the help of these concepts elements of the modern moral system inherent in the English-speaking community. To this end, a conceptual and cognitive-semantic analysis of contemporary English-language political discourse was carried out on the basis of speeches delivered by delegates to the United Nations.The investigation is premised on the theory of conceptual metaphor, emphasizing the need to understand the metaphorical foundations of human consciousness and communication. The study collected and analyzed empirical data that can be used to draw conclusions about the models of representation and assessment of reality by members of the English-speaking community, which in turn opens up prospects for further research in a linguistic pragmatic way and studying the specific features of English-speakers’ view of the world.As a result of lexicological and discourse-based analysis of speech transcripts, the paper uncovers several basic metaphorical models (Morality as 1. Commitment; 2. Nurturant Parent; 3. Resilience; 4. Fairy Tale of the Just War; 5. Progress), which outline globalization within the conceptual view of the world and which are underlined by such antitheses as “moral - immoral,” “success - loss,” “strength - weakness” etc.