8,645 research outputs found
A case of pure apraxia of speech after left hemisphere stroke: behavioral findings and neural correlates
IntroductionApraxia of speech (AOS) is a motor speech disorder impairing the coordination of complex articulatory movements needed to produce speech. AOS typically co-occurs with a non-fluent aphasia, or language disorder, making it challenging to determine the specific brain structures that cause AOS. Cases of pure AOS without aphasia are rare but offer the best window into the neural correlates that support articulatory planning. The goal of the current study was to explore patterns of apraxic speech errors and their underlying neural correlates in a case of pure AOS.MethodsA 67-year-old right-handed man presented with severe AOS resulting from a fronto-insular lesion caused by an ischemic stroke. The participantâs speech and language were evaluated at 1-, 3- and 12-months post-onset. High resolution structural MRI, including diffusion weighted imaging, was acquired at 12âmonths post-onset.ResultsAt the first assessment, the participant made minor errors on the Comprehensive Aphasia Test, demonstrating mild deficits in writing, auditory comprehension, and repetition. By the second assessment, he no longer had aphasia. On the Motor Speech Evaluation, the severity of his AOS was initially rated as 5 (out of 7) and improved to a score of 4 by the second visit, likely due to training by his SLP at the time to slow his speech. Structural MRI data showed a fronto-insular lesion encompassing the superior precentral gyrus of the insula and portions of the inferior and middle frontal gyri and precentral gyrus. Tractography derived from diffusion MRI showed partial damage to the frontal aslant tract and arcuate fasciculus along the white matter projections to the insula.DiscussionThis pure case of severe AOS without aphasia affords a unique window into the behavioral and neural mechanisms of this motor speech disorder. The current findings support previous observations that AOS and aphasia are dissociable and confirm a role for the precentral gyrus of the insula and BA44, as well as underlying white matter in supporting the coordination of complex articulatory movements. Additionally, other regions including the precentral gyrus, Brocaâs area, and Area 55b are discussed regarding their potential role in successful speech production
An American Knightmare: Joker, Fandom, and Malicious Movie Meaning-Making
This monograph concerns the long-standing communication problem of how individuals can identify and resist the influence of unethical public speakers. Scholarship on the issue of what Socrates & Plato called the âEvil Loverâ â i.e., the ill-intended rhetor â began with the Greek philosophers, but has carried into [post]Modern anxieties. For instance, the study of Nazi propaganda machines, and the rhetoric of Hitler himself, rejuvenated interest in the study of speech and communication in the U.S. and Europe. Whereas unscrupulous sophists used lectures and legal forums, and Hitler used a microphone, contemporary Evil Lovers primarily draw on new, internet-related tools to share their malicious influence. These new tools of influence are both more far-reaching and more subtle than the traditional practices of listening to a designated speaker appearing at an overtly political event. Rhetorician Ashley Hinck has recently noted the ways that popular culture â communication about texts which are commonly accessible and shared â are now significant sites through which citizens learn moral and political values. Accordingly, the talk of internet influencers who interpret popular texts for other fans has the potential to constitute strong persuasive power regarding ethics and civic responsibility.
The present work identifies and responds to a particular case example of popular culture text that has been recently, and frequently, leveraged in moral and civic discourses: Todd Phillipsâ Joker. Specifically, this study takes a hermeneutic approach to understanding responses, especially those explicitly invoking political ideology, to Joker as a method of examining civic meaning-making. A special emphasis is placed on the online film criticisms of Joker from white nationalist movie fans, who clearly exemplify ways that media responses can be leveraged by unethical speakers (i.e., Evil Lovers) and subtly diffused. The study conveys that these racist movie fans can embed values related to âtrolling,â incelism, and xenophobia into otherwise seemingly innocuous talk about film. While the sharing of such speech does not immediately mean its positive reception, this kind of communication yet constitutes a new and understudied attack on democratic values such as justice and equity. The case of white nationalist movie fan film criticism therefore reflects a particular brand of communicative strategy for contemporary Evil Lovers in communicating unethical messages under the covert guise of mundane movie talk
Generating Efficient Training Data via LLM-based Attribute Manipulation
In this paper, we propose a novel method, Chain-of-Thoughts Attribute
Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from
Large Language Models (LLMs). The main idea is to create data with changes only
in the attribute targeted by the task. Inspired by facial attribute
manipulation, our approach generates label-switched data by leveraging LLMs to
manipulate task-specific attributes and reconstruct new sentences in a
controlled manner. Instead of conventional latent representation controlling,
we implement chain-of-thoughts decomposition and reconstruction to adapt the
procedure to LLMs. Extensive results on text classification and other tasks
verify the advantage of CoTAM over other LLM-based text generation methods with
the same number of training examples. Analysis visualizes the attribute
manipulation effectiveness of CoTAM and presents the potential of LLM-guided
learning with even less supervision
Towards ending incarceration of Indigenous peoples in Canada: A critical, narrative inquiry of hegemonic power in the Gladue report process
Abstract
This study is concerned with the possibility that Gladue perpetuates the hegemonic powers of settler colonialism, white supremacy, patriarchy, and neoliberalism. Gladue is intended to remediate systemic anti-Indigenous racism by requiring judges to consider all alternatives to incarceration when sentencing Indigenous peoples, yet Indigenous incarceration rates continue to rise precipitously. On the surface, Gladue does not appear to disrupt the hegemonic status quo. How is it that the Canadian state, even when âremediating,â keeps producing the same â colonial, oppressive, and tyrannical â result?
This qualitative study used a critical, narrative methodology, interviewing Gladue report writers (n=9) and judges (n=12) about their perspectives and experiences with Gladue, particularly Gladue reports. The study purposefully emphasized settler accountability â research as reparation â in the research design, data collection, and analysis. A careful, ethical protocol for researching with Indigenous peoples (n=9) was followed, premised in Truth and Reconciliation âCall to Actionâ number 30 to reduce Indigenous incarceration in Canada.
This study found that Gladue is falling short of achieving its systemic aim because of (a) a hyper-individualistic, dehumanizing configuration that discursively shifts judges away from dealing with the systemic issue of anti-Indigenous racism, towards judging the individual Indigenous person before the court; (b) colonial mentalities (e.g., whiteness and patriarchy) persisting in the process; (c) a lack of funding for Gladue writers, as well alternatives to incarceration, constraining judgesâ capacities to divert Indigenous away from prisons. The study points towards the need for a more radical framework for Gladue that honours Indigenous self-determination and foundational treaties such as the Two Row Wampum
Applying a proven filtering method to adjust the training sample of neural networks
The article notes the complexity and duration of the process of forming a training sample of a neural network, since the correctness of the sample is checked by assessing the quality of the network after its training, it also notes the negative impact of the commonly used formal method of forming a training sample of a neural network without taking into account the physical processes of data and signal transformation in real devices on the quality of the network when filtering the noise of the speech signal. Methods and means for filtering the noise of speech signals are described. To solve the filtering problem, a sequence of main stages of processing a speech signal containing noise is presented, and their description is given. The article proposes to choose a filtering method based on the analysis of noise characteristics, while it is recommended to distinguish between homogeneous (monotonic) and dynamically changing (random) noise, for which filtering methods are different. When choosing a filtering method, it is proposed to take into account the degree of correspondence between the frequency range of the noise and the speech signal. As the main way to reduce noise, an approach is proposed based on the use of an improved and proven method for filtering noise by subtracting the spectral components of noise from the spectrum of a signal containing noise. This approach is proposed to be used for the formation and correction of a training set for a neural network designed to reduce noise in a speech signal. The results of the practical application of the proven filtration method confirmed the feasibility of its application. An important result of the work presented in the article is the possibility of evaluating the feasibility of specific corrective changes in the neural network training set by comparing it with the filtering results of the modified and tested method
Automatic Question Generation to Support Reading Comprehension of Learners - Content Selection, Neural Question Generation, and Educational Evaluation
Simply reading texts passively without actively engaging with their content is suboptimal for text comprehension since learners may miss crucial concepts or misunderstand essential ideas.
In contrast, engaging learners actively by asking questions fosters text comprehension.
However, educational resources frequently lack questions.
Textbooks often contain only a few at the end of a chapter, and informal learning resources such as Wikipedia lack them entirely.
Thus, in this thesis, we study to what extent questions about educational science texts can be automatically generated, tackling two research questions.
The first question concerns selecting learning-relevant passages to guide the generation process.
The second question investigates the generated questions' potential effects and applicability in reading comprehension scenarios.
Our first contribution improves the understanding of neural question generation's quality in education.
We find that the generators' high linguistic quality transfers to educational texts but that they require guidance by educational content selection.
In consequence, we study multiple educational context and answer selection mechanisms.
In our second contribution, we propose novel context selection approaches which target question-worthy sentences in texts.
In contrast to previous works, our context selectors are guided by educational theory.
The proposed methods perform competitive to related work while operating with educationally motivated decision criteria that are easier to understand for educational experts.
The third contribution addresses answer selection methods to guide neural question generation with expected answers.
Our experiments highlight the need for educational corpora for the task. Models trained on noneducational corpora do not transfer well to the educational domain.
Given this discrepancy, we propose a novel corpus construction approach.
It automatically derives educational answer selection corpora from textbooks.
We verify the approach's usefulness by showing that neural models trained on the constructed corpora learn to detect learning-relevant concepts.
In our last contribution, we use the insights from the previous experiments to design, implement, and evaluate an automatic question generator for educational use.
We evaluate the proposed generator intrinsically with an expert annotation study and extrinsically with an empirical reading comprehension study.
The two evaluation scenarios provide a nuanced view of the generated questions' strengths and weaknesses.
Expert annotations attribute an educational value to roughly 60 % of the questions but also reveal various ways in which the questions still fall short of the quality experts desire.
Furthermore, the reader-based evaluation indicates that the proposed educational question generator increases learning outcomes compared to a no-question control group.
In summary, the results of the thesis improve the understanding of the content selection tasks in educational question generation and provide evidence that it can improve reading comprehension.
As such, the proposed approaches are promising tools for authors and learners to promote active reading and thus foster text comprehension
On the Principles of Evaluation for Natural Language Generation
Natural language processing is concerned with the ability of computers to understand natural language texts, which is, arguably, one of the major bottlenecks in the course of chasing the holy grail of general Artificial Intelligence. Given the unprecedented success of deep learning technology, the natural language processing community has been almost entirely in favor of practical applications with state-of-the-art systems emerging and competing for human-parity performance at an ever-increasing pace. For that reason, fair and adequate evaluation and comparison, responsible for ensuring trustworthy, reproducible and unbiased results, have fascinated the scientific community for long, not only in natural language but also in other fields. A popular example is the ISO-9126 evaluation standard for software products, which outlines a wide range of evaluation concerns, such as cost, reliability, scalability, security, and so forth. The European project EAGLES-1996, being the acclaimed extension to ISO-9126, depicted the fundamental principles specifically for evaluating natural language technologies, which underpins succeeding methodologies in the evaluation of natural language.
Natural language processing encompasses an enormous range of applications, each with its own evaluation concerns, criteria and measures. This thesis cannot hope to be comprehensive but particularly addresses the evaluation in natural language generation (NLG), which touches on, arguably, one of the most human-like natural language applications. In this context, research on quantifying day-to-day progress with evaluation metrics lays the foundation of the fast-growing NLG community. However, previous works have failed to address high-quality metrics in multiple scenarios such as evaluating long texts and when human references are not available, and, more prominently, these studies are limited in scope, given the lack of a holistic view sketched for principled NLG evaluation.
In this thesis, we aim for a holistic view of NLG evaluation from three complementary perspectives, driven by the evaluation principles in EAGLES-1996: (i) high-quality evaluation metrics, (ii) rigorous comparison of NLG systems for properly tracking the progress, and (iii) understanding evaluation metrics. To this end, we identify the current state of challenges derived from the inherent characteristics of these perspectives, and then present novel metrics, rigorous comparison approaches, and explainability techniques for metrics to address the identified issues.
We hope that our work on evaluation metrics, system comparison and explainability for metrics inspires more research towards principled NLG evaluation, and contributes to the fair and adequate evaluation and comparison in natural language processing
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
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