Automatic Severity Assessment of Dysarthric speech by using Self-supervised Model with Multi-task Learning

Automatic assessment of dysarthric speech is essential for sustained treatments and rehabilitation. However, obtaining atypical speech is challenging, often leading to data scarcity issues. To tackle the problem, we propose a novel automatic severity assessment method for dysarthric speech, using the self-supervised model in conjunction with multi-task learning. Wav2vec 2.0 XLS-R is jointly trained for two different tasks: severity level classification and an auxilary automatic speech recognition (ASR). For the baseline experiments, we employ hand-crafted features such as eGeMaps and linguistic features, and SVM, MLP, and XGBoost classifiers. Explored on the Korean dysarthric speech QoLT database, our model outperforms the traditional baseline methods, with a relative percentage increase of 4.79% for classification accuracy. In addition, the proposed model surpasses the model trained without ASR head, achieving 10.09% relative percentage improvements. Furthermore, we present how multi-task learning affects the severity classification performance by analyzing the latent representations and regularization effect

Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification

This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes Uncertainty Quantification (UQ) for automatic speech intelligibility assessment for dysarthric speech. Current GoP methods rely heavily on neural network-driven overconfident predictions, which is unsuitable for assessing dysarthric speech due to its significant acoustic differences from healthy speech. To alleviate the problem, UQ techniques were used on GoP by 1) normalizing the phoneme prediction (entropy, margin, maxlogit, logit-margin) and 2) modifying the scoring function (scaling, prior normalization). As a result, prior-normalized maxlogit GoP achieves the best performance, with a relative increase of 5.66%, 3.91%, and 23.65% compared to the baseline GoP for English, Korean, and Tamil, respectively. Furthermore, phoneme analysis is conducted to identify which phoneme scores significantly correlate with intelligibility scores in each language.Comment: Accepted to Interspeech 202

Androgen-dependent mammary carcinogenesis in rats transgenic for the Neu proto-oncogene

AbstractTransgenic rats were created with overexpression of the Neu proto-oncogene in the mammary gland of both sexes, yet only males developed mammary cancer in an androgen-dependent fashion. Transgenic females only developed mammary cancer if treated with androgens. These tumors were positive for androgen receptor (AR), but negative for estrogen and progesterone receptors. Extensive analysis failed to detect mutations anywhere within the neu transgene from mammary carcinomas. Established mammary carcinomas eventually escaped their dependency on androgens. Transgenic long-term gonadectomized rats did not develop mammary cancer, but Neu overexpression stimulated the growth of their mammary glands. Our results suggest crosstalk between the Neu proto-oncogene and AR signaling pathways in the growth of both the normal and cancerous mammary epithelium

DiffFace: Diffusion-based Face Swapping with Facial Guidance

In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the desired identity. In the sampling process, we use the off-the-shelf facial expert models to make the model transfer source identity while preserving target attributes faithfully. During this process, to preserve the background of the target image and obtain the desired face swapping result, we additionally propose a target-preserving blending strategy. It helps our model to keep the attributes of the target face from noise while transferring the source facial identity. In addition, without any re-training, our model can flexibly apply additional facial guidance and adaptively control the ID-attributes trade-off to achieve the desired results. To the best of our knowledge, this is the first approach that applies the diffusion model in face swapping task. Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability. Extensive experiments show that our DiffFace is comparable or superior to the state-of-the-art methods on several standard face swapping benchmarks.Comment: Project Page: https://hxngiee.github.io/DiffFac

Ambipolar organic field-effect transistors fabricated using a composite of semiconducting polymer and soluble fullerene

Organic field-effect transistors (FETs) with equivalent hole and electron mobilities have been demonstrated. The devices were fabricated using a phase separated mixture of regioregular poly(3-hexylthiophene) and [6,6]-phenyl C-61-butyric acid methyl ester as the active layer and using aluminum (Al) for the source and drain electrodes. Measurements of the source-drain current versus gate voltage gave an electron mobility of mu(e)=2.0x10(-3) cm(2)/V s and hole mobility of mu(h)=1.7x10(-3)cm(2)/V s. The ambipolar FET properties arise from the use of Al electrodes for the source and drain; the contacts between the Al electrodes and the active layer are improved by thermal annealing at elevated temperatures (150 degrees C), thereby enabling balanced injection for both holes and electrons in a single device.open413

Photovoltaic effects on the organic ambipolar field-effect transistors

An organic multifunctional device, which can function as an ambipolar field-effect transistor (FET) and a photovoltaic (PV) cell, has been demonstrated using a phase separated mixture of poly(3-hexylthiophene) and [6,6]-phenyl C-61-butyric acid methyl ester. The gold (Au) electrode used for hole injection in the FET mode (source) acts as the anode in PV cell mode, and the aluminum (Al) electrode for electron injection in the FET mode (drain) acts as the cathode in PV cell mode. The device exhibits clear PV phenomena under illumination at zero gate bias with a power conversion efficiency of 0.6% as well as the properties of an ambipolar FET when the gate bias is applied.open292

Improved electron injection in polymer light-emitting diodes using anionic conjugated polyelectrolyte

We report improved performance in polymer light-emitting diodes incorporating conjugated polyelectrolytes as an electron injection layer (EIL). When we introduce water soluble conjugated polymers, poly[9,9'-bis(4-sulfonatobutyl)fluorene-co-alt-1,4-phenylene] (anionic PFP), between the aluminum (Al) cathode and emissive layer, the devices show an increased electroluminescence efficiency with a lowered turn-on voltage. We believe the mobile Na(+) ions in the EIL layer directly influences the device efficiency by forming a low work function layer at the interface between the EIL and Al cathode, thereby facilitating the electron injection into the emissive layer.open141

Electroluminescence in polymer-fullerene photovoltaic cells

We report electroluminescence (EL) in photovoltaic (PV) cells based on semiconducting polymer-fullerene composites. By applying a forward bias to the PV cells, the devices exhibited a clear EL action with a peak around 1.5 eV. We ascribe this peak to an "electric field-assisted exciplex" formed between the electrons in the fullerenes and the holes in the polymers, thereby resulting in radiative recombination in the composites. This finding is totally unexpected because of a strong photoluminescence quenching in the same materials. Since the same devices also showed typical photovoltaic effects under illumination, our results demonstrate a dual functionality in one device; polymer photovoltaic cells and polymer light-emitting diodes.open464