UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization

Dysarthric speech reconstruction (DSR) systems aim to automatically convert dysarthric speech into normal-sounding speech. The technology eases communication with speakers affected by the neuromotor disorder and enhances their social inclusion. NED-based (Neural Encoder-Decoder) systems have significantly improved the intelligibility of the reconstructed speech as compared with GAN-based (Generative Adversarial Network) approaches, but the approach is still limited by training inefficiency caused by the cascaded pipeline and auxiliary tasks of the content encoder, which may in turn affect the quality of reconstruction. Inspired by self-supervised speech representation learning and discrete speech units, we propose a Unit-DSR system, which harnesses the powerful domain-adaptation capacity of HuBERT for training efficiency improvement and utilizes speech units to constrain the dysarthric content restoration in a discrete linguistic space. Compared with NED approaches, the Unit-DSR system only consists of a speech unit normalizer and a Unit HiFi-GAN vocoder, which is considerably simpler without cascaded sub-modules or auxiliary tasks. Results on the UASpeech corpus indicate that Unit-DSR outperforms competitive baselines in terms of content restoration, reaching a 28.2% relative average word error rate reduction when compared to original dysarthric speech, and shows robustness against speed perturbation and noise.Comment: Accepted to ICASSP 202

Plausible GMM:A Quasi-Bayesian Approach

Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, verify that quasi-posteriors can be used to obtain approximately optimal Bayesian decision rules under the maintained prior structure over misspecification, and provide a form of frequentist coverage results. We illustrate the approach through empirical examples where we obtain informative inference for structural objects allowing for substantial relaxations of the requirement that moment conditions hold exactly

Large Language Model-based FMRI Encoding of Language Functions for Subjects with Neurocognitive Disorder

Functional magnetic resonance imaging (fMRI) is essential for developing encoding models that identify functional changes in language-related brain areas of individuals with Neurocognitive Disorders (NCD). While large language model (LLM)-based fMRI encoding has shown promise, existing studies predominantly focus on healthy, young adults, overlooking older NCD populations and cognitive level correlations. This paper explores language-related functional changes in older NCD adults using LLM-based fMRI encoding and brain scores, addressing current limitations. We analyze the correlation between brain scores and cognitive scores at both whole-brain and language-related ROI levels. Our findings reveal that higher cognitive abilities correspond to better brain scores, with correlations peaking in the middle temporal gyrus. This study highlights the potential of fMRI encoding models and brain scores for detecting early functional changes in NCD patients.Comment: 5 pages, accepted by Interspeech 202

P2 receptor mRNA expression profiles in human lymphocytes, monocytes and CD34+ stem and progenitor cells

BACKGROUND: Extracellular nucleotides (ATP, ADP, UTP and UDP) exert a wide range of biological effects in blood cells mediated by multiple ionotropic P2X receptors and G protein-coupled P2Y receptors. Although pharmacological experiments have suggested the presence of several P2 receptor subtypes on monocytes and lymphocytes, some results are contradictory. Few physiological functions have been firmly established to a specific receptor subtype, partly because of a lack of truly selective agonists and antagonists. This stimulated us to investigate the expression of P2X and P2Y receptors in human lymphocytes and monocytes with a newly established quantitative mRNA assay for P2 receptors. In addition, we describe for the first time the expression of P2 receptors in CD34(+ )stem and progenitor cells implicating a potential role of P2 receptors in hematopoietic lineage and progenitor/stem cell function. RESULTS: Using a quantitative mRNA assay, we assessed the hypothesis that there are specific P2 receptor profiles in inflammatory cells. The P2X(4 )receptor had the highest expression in lymphocytes and monocytes. Among the P2Y receptors, P2Y(12 )and P2Y(2 )had highest expression in lymphocytes, while the P2Y(2 )and P2Y(13 )had highest expression in monocytes. Several P2 receptors were expressed (P2Y(2), P2Y(1), P2Y(12), P2Y(13), P2Y(11), P2X(1), P2X(4)) in CD34+ stem and progenitor cells. CONCLUSIONS: The most interesting findings were the high mRNA expression of P2Y(12 )receptors in lymphocytes potentially explaining the anti-inflammatory effects of clopidogrel, P2Y(13 )receptors in monocytes and a previously unrecognised expression of P2X(4 )in lymphocytes and monocytes. In addition, for the first time P2 receptor mRNA expression patterns was studied in CD34(+ )stem and progenitor cells. Several P2 receptors were expressed (P2Y(2), P2Y(1), P2Y(12), P2Y(13), P2Y(11), P2X(1), P2X(4)), indicating a role in differentiation and proliferation. Thus, it is possible that specific antibodies to P2 receptors could be used to identify progenitors for monocytes, lymphocytes and megakaryocytes

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.Comment: Pre-print version of a ACM SIGSPATIAL 2020 poster [paper](https://dl.acm.org/doi/10.1145/3397536.3422208). The code is available at [Github](https://github.com/shawnwang-tech/PM2.5-GNN), and the talk is available at [YouTube](https://www.youtube.com/watch?v=VX93vMthkGM

GWO-BP neural network based OP performance prediction for mobile multiuser communication networks

The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction