Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring

Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i.e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation. In this paper, we propose to use linguistic-acoustic similarity to explicitly measure the deviation of non-native production from its native reference for pronunciation assessment. Specifically, the deviation is first estimated by the cosine similarity between reference phone embedding and corresponding acoustic embedding. Next, a phone-level Goodness of pronunciation (GOP) pre-training stage is introduced to guide this similarity-based learning for better initialization of the aforementioned two embeddings. Finally, a transformer-based hierarchical pronunciation scorer is used to map a sequence of phone embeddings, acoustic embeddings along with their similarity measures to predict the final utterance-level score. Experimental results on the non-native databases suggest that the proposed system significantly outperforms the baselines, where the acoustic and phone embeddings are simply added or concatenated. A further examination shows that the phone embeddings learned in the proposed approach are able to capture linguistic-acoustic attributes of native pronunciation as reference.Comment: Accepted by ICASSP 202

An ASR-free Fluency Scoring Approach with Self-Supervised Learning

A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach. This paper describes a novel ASR-free approach for automatic fluency assessment using self-supervised learning (SSL). Specifically, wav2vec2.0 is used to extract frame-level speech features, followed by K-means clustering to assign a pseudo label (cluster index) to each frame. A BLSTM-based model is trained to predict an utterance-level fluency score from frame-level SSL features and the corresponding cluster indexes. Neither speech transcription nor time stamp information is required in the proposed system. It is ASR-free and can potentially avoid the ASR errors effect in practice. Experimental results carried out on non-native English databases show that the proposed approach significantly improves the performance in the "open response" scenario as compared to previous methods and matches the recently reported performance in the "read aloud" scenario.Comment: Accepted by ICASSP 202

Modeling Hidden Nodes Collisions in Wireless Sensor Networks: Analysis Approach

This paper studied both types of collisions. In this paper, we show that advocated solutions for coping with hidden node collisions are unsuitable for sensor networks. We model both types of collisions and derive closed-form formula giving the probability of hidden and visible node collisions. To reduce these collisions, we propose two solutions. The first one based on tuning the carrier sense threshold saves a substantial amount of collisions by reducing the number of hidden nodes. The second one based on adjusting the contention window size is complementary to the first one. It reduces the probability of overlapping transmissions, which reduces both collisions due to hidden and visible nodes. We validate and evaluate the performance of these solutions through simulations

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

Targeting BDNF with acupuncture: A novel integrated strategy for diabetes and depression comorbidity

Diabetes and depression are common comorbid conditions that impose a substantial health burden. Acupuncture may effectively improve symptoms in patients with diabetes and depression, but the underlying mechanism remains unclear. Brain-derived neurotrophic factor (BDNF) may play a vital role in the effects of acupuncture on diabetes and depression comorbidity. This review summarizes the potential role of BDNF in acupuncture for diabetes and depression comorbidity. BDNF appears to exert its effects via the BDNF-TrkB-ERK-CREB signaling pathway. BDNF levels are reduced in diabetes and depression, and acupuncture may increase BDNF expression, improving symptoms and glycemic control. High-quality research is needed to validate the efficacy of acupuncture for diabetes and depression comorbidity. Randomized controlled trials and mechanistic studies should investigate the BDNF pathway and other potential mechanisms. Improved understanding of the links between diabetes, depression and acupuncture may enable targeted and individualized patient care. Earlier diagnosis and management of diabetes and depression comorbidity should also be a priority

Identification and assessment of TCR-T cells targeting an epitope conserved in SARS-CoV-2 variants for the treatment of COVID-19

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global public health challenge, with the emergence of variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Current vaccines or monoclonal antibodies may not well be protect against infection with new SARS-CoV-2 variants. Unlike antibody-based treatment, T cell-based therapies such as TCR-T cells can target epitopes that are highly conserved across different SARS-CoV-2 variants. Reportedly, T cell-based immunity alone can restrict SARS-CoV-2 replication. METHODS: In this study, we identified two TCRs targeting the RNA-dependent RNA polymerase (RdRp) protein in CD8 + T cells. Functional evaluation by transducing these TCRs into CD8 + or CD4 + T cells confirmed their specificity. RESULTS: Combinations of inflammatory and anti-inflammatory cytokines secreted by CD8 + and CD4 + T cells can help control COVID-19 in patients. Moreover, the targeted epitope is highly conserved in all emerged SARS-CoV-2 variants, including the Omicron. It is also conserved in the seven coronaviruses that infect humans and more broadly in the subfamily Coronavirinae. CONCLUSIONS: The pan-genera coverage of mutant epitopes from the Coronavirinae subfamily by the two TCRs highlights the unique strengths of TCR-T cell therapies in controlling the ongoing pandemic and in preparing for the next coronavirus outbreak

Large π‑Conjugated Quinacridone Derivatives: Syntheses, Characterizations, Emission, and Charge Transport Properties

Two 11-ring-fused quinacridone derivatives, TTQA and DCNTTQA, have been synthesized by ferric chloride mediated cyclization and Knoevenagel reaction. Replacement of the carbonyl groups (in TTQA) with dicyanoethylene groups (in DCNTTQA) not only red-shifted the emission to the near-infrared region but also led to a nonplanar skeleton that significantly improved the solubility of DCNTTQA. Moreover, dicyanoethylene groups rendered DCNTTQA low-lying HOMO and LUMO levels. DCNTTQA-based solution-processed field-effect transistors showed a hole mobility up to 0.217 cm² V^–1 s^–1