Korean Passive Sentence Comprehension Deficits and its Relation to Working Memory Capacity in Persons with Aphasia

The current study investigated Korean passive sentence comprehension deficits in aphasia and its underlying processing mechanisms using three types of syntactic structures: 1) active sentences with a 2-argument structure, 2) active sentences with a 3-argument structure, and 3) passive counterparts of active sentences with a 2-argument structure. Persons with aphasia showed differentially greater difficulties in passive than 2-place active sentences compared to the normal elderly adults, but the group differences were not significant between the passive and 3-place active sentences. Working memory, not the short-term memory, was significantly correlated with overall aphasia severity and performance on sentence comprehension tasks

Development of a multimedia model (POPsLTEA) to assess the influence of climate change on the fate and transport of polycyclic aromatic hydrocarbons in East Asia

A dynamic multimedia model (POPsLTEA) for an East Asia region was developed and evaluated to quantitatively assess how climate change (CC) alters the environmental fate and transport dynamics of 16 polycyclic aromatic hydrocarbons (PAHs) in air, water, soil, and sediment. To cover the entire model domain (25°N-50°N and 98°E-148°E) where China, Japan, and South and North Koreas are of primary concern, a total of 5000 main cells of 50 km × 50 km size were used while 1008 cells of a finer spatial resolution (12.5 km × 12.5 km) was nested for South Korea (33°N-38°N and 126°E-132°E). Most of the predicted concentrations agreed with the observed values within one order of magnitude with a tendency of overestimation for air and sediment. Prediction of the atmospheric concentration was statistically significant in both coincidence and association, suggesting the model's potential to successfully predict the fate and transport of the PAHs as influenced by CC. An example study of benzo(a)pyrene demonstrates that direction and strength of the CC influence on the pollution levels vary with the location and environmental media. As compared to the five year period of 2011 to 2015, the changes across the model domain in the annual geometric mean concentration over the years of 2021 through 2100 were predicted to range from 88% to 304%, from 84% to 109%, from 32% to 362%, and from 49% to 303%, in air, soil, surface water, and sea water, respectively, under the scenario of RCP8.5.OAIID:RECH_ACHV_DSTSH_NO:T201700197RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A003201CITE_RATE:3.976FILENAME:published main article.pdfDEPT_NM:환경계획학과EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/a615ac5c-bdd0-4856-8433-f28d135249e3/linkCONFIRM:

High-resolution embedding extractor for speaker diarisation

Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each speech segment. However, because of the sliding window approach, a segment easily includes two or more speakers owing to speaker change points. This study proposes a novel embedding extractor architecture, referred to as a high-resolution embedding extractor (HEE), which extracts multiple high-resolution embeddings from each speech segment. Hee consists of a feature-map extractor and an enhancer, where the enhancer with the self-attention mechanism is the key to success. The enhancer of HEE replaces the aggregation process; instead of a global pooling layer, the enhancer combines relative information to each frame via attention leveraging the global context. Extracted dense frame-level embeddings can each represent a speaker. Thus, multiple speakers can be represented by different frame-level features in each segment. We also propose an artificially generating mixture data training framework to train the proposed HEE. Through experiments on five evaluation sets, including four public datasets, the proposed HEE demonstrates at least 10% improvement on each evaluation set, except for one dataset, which we analyse that rapid speaker changes less exist.Comment: 5pages, 2 figure, 3 tables, submitted to ICASS

Massive MIMO Systems With Low-Resolution ADCs: Baseband Energy Consumption vs. Symbol Detection Performance

In massive multiple-input multiple-output (MIMO) systems using a large number of antennas, it would be difficult to connect high-resolution analog-to-digital converters (ADCs) to each antenna component due to high cost and energy consumption problems. To resolve these issues, there has been much work on implementing symbol detectors and channel estimators using low-resolution ADCs for massive MIMO systems. Although it is intuitively true that using low-resolution ADCs makes it possible to save a large amount of energy consumption in massive MIMO systems, the relationship between energy consumption using low-resolution ADCs and detection performance has not been properly analyzed yet. In this paper, the tradeoff between different detectors and total baseband energy consumption including flexible ADCs is thoroughly analyzed taking the optimal fixed-point operations performed during the detection processes into account. In order to minimize the energy consumption for the given channel condition, the proposed scheme selects the best mode among various processing options while supporting the target frame error rate. The numerous case studies reveal that the proposed work remarkably saves the energy consumption of the massive MIMO processing compared with the existing schemes.11Ysciescopu

Absolute decision corrupts absolutely: conservative online speaker diarisation

Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early phase of an input session can lead to catastrophic results. We hypothesise that cautiously increasing the number of estimated speakers is of paramount importance among many other factors. Thus, our proposed framework includes decreasing the number of speakers by one when the system judges that an increase in the past was faulty. We also adopt dual buffers, checkpoints and centroids, where checkpoints are combined with silhouette coefficients to estimate the number of speakers and centroids represent speakers. Again, we believe that more than one centroid can be generated from one speaker. Thus we design a clustering-based label matching technique to assign labels in real-time. The resulting system is lightweight yet surprisingly effective. The system demonstrates state-of-the-art performance on DIHARD 2 and 3 datasets, where it is also competitive in AMI and VoxConverse test sets.Comment: 5pages, 2 figure, 4 tables, submitted to ICASS

Disentangled dimensionality reduction for noise-robust speaker diarisation

The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation systems, but they often capture spurious information such as noise and reverberation, adversely affecting performance. Our previous work has proposed an auto-encoder-based dimensionality reduction module to help remove the redundant information. However, they do not explicitly separate such information and have also been found to be sensitive to hyper-parameter values. To this end, we propose two contributions to overcome these issues: (i) a novel dimensionality reduction framework that can disentangle spurious information from the speaker embeddings; (ii) the use of a speech/non-speech indicator to prevent the speaker code from representing the background noise. Through a range of experiments conducted on four different datasets, our approach consistently demonstrates the state-of-the-art performance among models without system fusion.Comment: This paper was submitted to Interspeech202