10,155 research outputs found

    Expressive recall and recognition as complementary measures to assess novel word learning ability in aphasia

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    Novel word learning ability has been associated with language treatment outcomes in people with aphasia (PWA), and its assessment could inform prognosis and rehabilitation. We used a brief experimental task to examine novel word learning in PWA, determine the value of phonological cueing in assessing learning outcomes, and identify factors that modulate learning ability. Twelve PWA and nineteen healthy controls completed the task, and recall and recognition tests of learning ability. Most PWA showed comparable learning outcomes to those of the healthy controls. Learning assessed via expressive recall was more clearly evidenced with phonological cues. Better single word processing abilities and phonological short term memory and higher integrity of the left inferior frontal gyrus were related to better learning performance. Brief learning tasks like this one are clinically feasible and hold promise as screening tools of verbal learning in PWA once validated and evaluated for their capacity to predict treatment outcomes

    Linking language and emotion: how emotion is understood in language comprehension, production and prediction using psycholinguistic methods

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    Emotions are an integral part of why and how we use language in everyday life. We communicate our concerns, express our woes, and share our joy through the use of non-verbal and verbal language. Yet there is a limited understanding of when and how emotional language is processed differently to neutral language, or of how emotional information facilitates or inhibits language processing. Indeed, various efforts have been made to bring back emotions into the discipline of psycholinguistics in the last decade. This can be seen in many interdisciplinary models focusing on the role played by emotion in each aspect of linguistic experience. In this thesis, I answer this call and pursue questions that remain unanswered in psycholinguistics regarding its interaction with emotion. The general trend that I am using to bring emotion into psycholinguistic research is straightforward. Where applicable and relevant, I use well-established tasks or paradigms to investigate the effects of emotional content in language processing. Hence, I focused on three main areas of language processing: comprehension, production and prediction. The first experimental chapter includes a series of experiments utilising the Modality Switching Paradigm to investigate whether sentences describing emotional states are processed differently from sentences describing cognitive states. No switching effects were found consistently in my 3 experiments. My results suggest that these distinct classes of interoceptive concepts, such as ‘thinking’ or ‘being happy’, are not processed differently from each other, suggesting that people do not switch attention between different interoceptive systems when comprehending emotional or cognitive sentences. I discuss the implications for grounded cognition theory in the embodiment literature. In my second experimental chapter, I used the Cumulative Semantic Interference Paradigm to investigate these two questions: (1) whether emotion concepts interfere with one another when repeatedly retrieved (emotion label objects), and (2) whether similar interference occurs for concrete objects that share similar valence association (emotion-laden objects). This could indicate that people use information such as valence and arousal to group objects in semantic memory. I found that interference occurs when people retrieve direct emotion labels repeatedly (e.g., “happy” and “sad”) but not when they retrieve the names of concrete objects that have similar emotion connotations (e.g., “puppy” and “rainbow”). I discuss my findings in terms of the different types of information that support representation of abstract vs. concrete concepts. In my final experimental chapter, I used the Visual World Paradigm to investigate whether the emotional state of an agent is used to inform predictions during sentence processing. I found that people do use the description of emotional state of an agent (e.g., “The boy is happy”) to predict the cause of that affective state during sentence processing (e.g., “because he was given an ice-cream”). A key result here is that people were more likely to fixate on the emotionally congruent objects (e.g., ice-cream) compared to incongruent objects (e.g., broccoli). This suggests that people rapidly and automatically inform predictions about upcoming sentence information based on the emotional state of the agent. I discuss our findings as a novel contribution to the Visual World literature. I conducted a diverse set of experiments using a range of established psycholinguistic methods to investigate the roles of emotional information in language processing. I found clear results in the eye-tracking study but inconsistent effects in both switching and interference studies. I interpret these mixed findings in the following way: emotional content does not always have effects in language processing and that effect are most likely in tasks that explicitly require participants to simulate emotion states in some way. Regardless, not only was I successful in finding some novel results by extending previous tasks, but I was also able to show that this is an avenue that can be explored more to advance the affective psycholinguistic field

    On the interplay between speech perception and production: insights from research and theories

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    The study of spoken communication has long been entrenched in a debate surrounding the interdependence of speech production and perception. This mini review summarizes findings from prior studies to elucidate the reciprocal relationships between speech production and perception. We also discuss key theoretical perspectives relevant to speech perception-production loop, including hyper-articulation and hypo-articulation (H&H) theory, speech motor theory, direct realism theory, articulatory phonology, the Directions into Velocities of Articulators (DIVA) and Gradient Order DIVA (GODIVA) models, and predictive coding. Building on prior findings, we propose a revised auditory-motor integration model of speech and provide insights for future research in speech perception and production, focusing on the effects of impaired peripheral auditory systems

    Bruno Latour and Artificial Intelligence

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    This scenario discusses generative AI in light of Bruno Latour’s sociology of technology. It considers why Latour showed little interest in the simulation of intelligence and how connectionist AI fails to meet his condition for scientificity but offers a fascinating writing mediation. AI is most interesting not because it emulates human thinking or writing, but because it differs from them. Drawing on actor-network theory, this scenario argues against the idea of machines becoming detached from their creators and highlights how AIs can only exist through the support of their human assistants. The risks associated with these technologies do not come from an improbable singularity, but from their embedding in the dull and exploitative industry of digital attention economy

    Towards Neuromorphic Gradient Descent: Exact Gradients and Low-Variance Online Estimates for Spiking Neural Networks

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    Spiking Neural Networks (SNNs) are biologically-plausible models that can run on low-powered non-Von Neumann neuromorphic hardware, positioning them as promising alternatives to conventional Deep Neural Networks (DNNs) for energy-efficient edge computing and robotics. Over the past few years, the Gradient Descent (GD) and Error Backpropagation (BP) algorithms used in DNNs have inspired various training methods for SNNs. However, the non-local and the reverse nature of BP, combined with the inherent non-differentiability of spikes, represent fundamental obstacles to computing gradients with SNNs directly on neuromorphic hardware. Therefore, novel approaches are required to overcome the limitations of GD and BP and enable online gradient computation on neuromorphic hardware. In this thesis, I address the limitations of GD and BP with SNNs by proposing three algorithms. First, I extend a recent method that computes exact gradients with temporally-coded SNNs by relaxing the firing constraint of temporal coding and allowing multiple spikes per neuron. My proposed method generalizes the computation of exact gradients with SNNs and enhances the tradeoffs between performance and various other aspects of spiking neurons. Next, I introduce a novel alternative to BP that computes low-variance gradient estimates in a local and online manner. Compared to other alternatives to BP, the proposed method demonstrates an improved convergence rate and increased performance with DNNs. Finally, I combine these two methods and propose an algorithm that estimates gradients with SNNs in a manner that is compatible with the constraints of neuromorphic hardware. My empirical results demonstrate the effectiveness of the resulting algorithm in training SNNs without performing BP

    A VOS analysis of LSTM Learners Classification for Recommendation System

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    In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research

    Adapting a Language Model While Preserving its General Knowledge

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    Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a particular domain to adapt the LM so that end-tasks in the domain can give improved performances. However, existing DA-training methods are in some sense blind as they do not explicitly identify what knowledge in the LM should be preserved and what should be changed by the domain corpus. This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge. Experimental results will demonstrate the effectiveness of the proposed approach.Comment: EMNLP 202

    Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis

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    Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount of training data using personalized text-to-speech synthesis. Here, we ask two fundamental questions about this strategy: when is synthetic data effective for personalization, and why is it effective in those cases? To address the first question, we adapt a state-of-the-art automatic speech recognition (ASR) model to target speakers from four benchmark datasets representative of different speaker types. We show that ASR personalization with synthetic data is effective in all cases, but particularly when (i) the target speaker is underrepresented in the global data, and (ii) the capacity of the global model is limited. To address the second question of why personalized synthetic data is effective, we use controllable speech synthesis to generate speech with varied styles and content. Surprisingly, we find that the text content of the synthetic data, rather than style, is important for speaker adaptation. These results lead us to propose a data selection strategy for ASR personalization based on speech content.Comment: ICASSP 202

    CTC-based Non-autoregressive Speech Translation

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    Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency. In this paper, we investigate the potential of connectionist temporal classification (CTC) for non-autoregressive speech translation (NAST). In particular, we develop a model consisting of two encoders that are guided by CTC to predict the source and target texts, respectively. Introducing CTC into NAST on both language sides has obvious challenges: 1) the conditional independent generation somewhat breaks the interdependency among tokens, and 2) the monotonic alignment assumption in standard CTC does not hold in translation tasks. In response, we develop a prediction-aware encoding approach and a cross-layer attention approach to address these issues. We also use curriculum learning to improve convergence of training. Experiments on the MuST-C ST benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67×\times, which is comparable to the autoregressive counterpart and even outperforms the previous best result of 0.9 BLEU points.Comment: ACL 2023 Main Conferenc
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