Word frequencies are integral in linguistic studies, showing strong correlations with speakers’ cognitive abilities and other important linguistic parameters including the Age of Acquisition (AoA). However, the formulation of credible Korean word frequency norms has been obstructed by the lack of expansive speech data and a reliable part-ofspeech (POS) tagger. In this study, we unveil Korean word frequency norms (KoFREN), derived from large-scale spontaneous speech corpora (41 million words) that include a balanced representation of gender and age. We employed a machine learning-powered POS tagger, showcasing accuracy on par with human annotators. Our frequency norms correlate significantly with external studies’ lexical decision time (LDT) and AoA measures. KoFREN also aligns with English counterparts sourced from SUBTLEX_US - an English word frequency measure that has been frequently used in the literature. KoFREN is poised to facilitate research in spontaneous Contemporary Korean and can be utilized in many fields, including clinical studies of Korean patients.

Social interaction quality ratings derived from short natural conversations can differentiate children with and without autism at the group level. In this work, we explored conversations between children and an unfamiliar adult who rated their social interaction success on six dimensions. Using hand-crafted acoustic and lexical features, we built different classifiers to predict children's dimensional conversation quality. The best classifier achieved 61% accuracy, which outperformed human raters (49%). Follow-up analyses revealed that a subset of features determined communication quality scores. Additionally, we extracted acoustic features using a pretrained audio transformer and improved our prediction to 68%. This study suggests that automatically predicting conversation quality could be an inexpensive and objective way to monitor intervention progress in children with communication challenges, and could be used to identify intervention targets for improving conversational success.

The Linguistic Data Consortium was founded in 1992 to solve the problem that limitations in access to shareable data was impeding progress in Human Language Technology research and development. At the time, DARPA had adopted the common task research management paradigm to impose additional rigor on their programs by also providing shared objectives, data and evaluation methods. Early successes underscored the promise of this paradigm but also the need for a standing infrastructure to host and distribute the shared data. During LDC’s initial five year grant, it became clear that the demand for linguistic data could not easily be met by the existing providers and that a dedicated data center could add capacity first for data collection and shortly thereafter for annotation. The expanding purview required expansions of LDC’s technical infrastructure including systems support and software development. An open question for the center would be its role in other kinds of research beyond data development. Over its 30 years history, LDC has performed multiple roles ranging from neutral, independent data provider to multisite programs, to creator of exploratory data in tight collaboration with system developers, to research group focused on data intensive investigations.

Trapping sets with short cycles are known to give a detrimental effect on the error floor performance of a low-density parity-check (LDPC) code. Unlike in binary LDPC codes., short cycles in a nonbinary low-density parity-check (NB-LDPC) code may be even more harmful to its performance if they do not satisfy the so-called full rank condition (FRC). This is because they may induce low-weight codewords or absorbing sets in that case. Thus, it is crucial to count the number of short cycles not satisfying the FRC as well as the number of short cycles for analyzing the performance of an NB-LDPC code. In this paper, we first develop a novel message-passing algorithm and identify how it is related to the FRC. We then propose a low-complexity algorithm for counting the number of short cycles not satisfying the FRC in an NB-LDPC code, as well as the number of short cycles.

Short cycles in a nonbinary low-density parity-check (NB-LDPC) code may be even more harmful to its performance if they do not satisfy the so-called full rank condition (FRC). This is because they may induce low-weight codewords or absorbing sets in that case. Thus, it is important to count the number of short cycles not satisfying the FRC as well as the number of short cycles for analyzing the performance of an NB-LDPC code. In this paper, we first develop a novel message-passing algorithm and identify how it is related to the FRC. We then propose a low-complexity algorithm for counting the number of short cycles not satisfying the FRC in an NB-LDPC code, as well as the number of short cycles. Finally, we propose a low-complexity algorithm for designing an NB-LDPC code with low error floor. Depending on the modulation scheme, the codes constructed by the proposed design algorithm have similar or slightly worse performance, compared with those constructed via the method by Poulliat et al. However, the proposed design algorithm does not require a cycle enumeration algorithm with high complexity, and therefore is feasible even in the case of large code length, say ≥5000.

The extended min-sum (EMS) algorithm for decoding low-density parity-check codes over the finite field with q elements significantly reduces decoding complexity by truncating each message of length q into a message of effective length nm. The number of effectively dominant components in each truncated message may gradually decrease with the number of decoding iterations. Based on this observation, we propose a novel adaptive EMS algorithm, called a two-length EMS (TL-EMS) algorithm. It chooses one of two candidate values as the effective message length nmfor each message by reflecting the concept called message separation. Numerical results show that it can significantly reduce the computational complexity with little performance degradation.

In cellular systems, the interference has been the most critical issue that practically limits the performance of overall networks. Furthermore, the distribution of the inter-cell interference (ICI) in conventional cellular networks employing orthogonal frequency-division multiple-access (OFDMA) with quadrature amplitude modulation (QAM) tends to approach a Gaussian distribution, which is known to be the worst-case distribution of the ICI as additive noise resulting in poor channel capacity. Thus, a dramatic enhancement of the channel capacity for the cellular network is expected when the ICI could be designed properly so that it has a non-Gaussian distribution. In this context, a cellular OFDMA system with a novel modulation scheme, frequency and quadrature-amplitude modulation (FQAM), is proposed in this paper. The statistical distribution of the ICI is shown to deviate far from the Gaussian distribution for the proposed FQAM-based system. Accordingly, it is shown that the non-Gaussian distribution of the ICI incurred with FQAM results in significantly improved transmission rates for the cell-edge users. Also, from the measurement results using practically implemented FQAM-based OFDMA systems, it is verified that the transmission rate for the cell-edge users could be increased significantly over the conventional QAM-based OFDMA system.

The distribution of the intercell interference (ICI) in conventional cellular networks employing orthogonal frequency-division multiple-access (OFDMA) with quadrature-amplitude modulation (QAM) tends to approach a Gaussian distribution when all available subcarriers in each cell are fully loaded. Recently, it has been also shown that the worst-case distribution of the ICI as additive noise in wireless networks with respect to the channel capacity is Gaussian. Thus, the channel capacity in cellular networks is expected to be further enhanced when the ICI could be designed properly so that it has a non-Gaussian distribution. This observation motivates us to propose, in this paper, a downlink cellular OFDMA network employing a modulation scheme called frequency and QAM (FQAM). We also derive maximum-likelihood metrics for the binary or non-binary error-correcting codes employed in the proposed network and propose their practical sub-optimal versions. Numerical results demonstrate that the distribution of the ICI in the proposed network deviates far from the Gaussian distribution. As a result, the transmission rates for the cell-edge users in the proposed network are significantly improved. In addition, the measurement results using practically implemented FQAM-based OFDMA systems verify that the transmission rates for the cell-edge users can dramatically increase, compared with the conventional QAM-based OFDMA network.