PromptTTS 2: Describing and Generating Voices with Text Prompt

Speech conveys more information than just text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompt for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompt based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available online\footnote{https://speechresearch.github.io/prompttts2}.Comment: Demo page: https://speechresearch.github.io/prompttts

High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks

Exact solutions of epidemic models are critical for identifying the severity and mitigation possibility for epidemics. However, solving complex models can be difficult when interfering conditions from the real-world are incorporated into the models. In this paper, we focus on the generally unsolvable adaptive susceptible-infected-susceptible (ASIS) epidemic model, a typical example of a class of epidemic models that characterize the complex interplays between the virus spread and network structural evolution. We propose two methods based on mean-field approximation, i.e., the first-order mean-field approximation (FOMFA) and higher-order mean-field approximation (HOMFA), to derive the exact solutions to ASIS models. Both methods demonstrate the capability of accurately approximating the metastable-state statistics of the model, such as the infection fraction and network density, with low computational cost. These methods are potentially powerful tools in understanding, mitigating, and controlling disease outbreaks and infodemics

Optimal Design of an Hourglass in-Fiber Air Fabry-Perot Microcavity—Towards Spectral Characteristics and Strain Sensing Technology

An hourglass in-fiber air microcavity Fabry-Perot interferometer is proposed in this paper, and its second reflecting surface of in-fiber microcavity is designed to be a concave reflector with the best curvature radius in order to improve the spectral characteristics. Experimental results proved that the extinction ratio of Fabry-Perot interferometer with cavity length of 60 μm and concave reflector radius of 60 μm is higher than for a rectangular Fabry-Perot interferometer with cavity length of 60 μm (14 dB: 11 dB). Theory and numerical simulation results show that the strain sensitivity of sensor can be improved by reducing the microcavity wall thickness and microcavity diameter, and when the in-fiber microcavity length is 40 μm, the microcavity wall thickness is 10 μm, the microcavity diameter is 20 μm, and the curvature radius of reflective surface II is 50 μm, the interference fringe contrast of is greater than 0.97, an Axial-pull sensitivity of 20.46 nm/N and resolution of 1 mN can be achieved in the range of 0–1 N axial tension. The results show that the performance of hourglass in-fiber microcavity interferometer is far superior to that of the traditional Fabry-Perot interferometer

Epidemic threshold and topological structure of susceptible-infectious-susceptible epidemics in adaptive networks

The interplay between disease dynamics on a network and the dynamics of the structure of that network characterizes many real-world systems of contacts. A continuous-time adaptive susceptible-infectious-susceptible (ASIS) model is introduced in order to investigate this interaction, where a susceptible node avoids infections by breaking its links to its infected neighbors while it enhances the connections with other susceptible nodes by creating links to them. When the initial topology of the network is a complete graph, an exact solution to the average metastable-state fraction of infected nodes is derived without resorting to any mean-field approximation. A linear scaling law of the epidemic threshold ?c as a function of the effective link-breaking rate ? is found. Furthermore, the bifurcation nature of the metastable fraction of infected nodes of the ASIS model is explained. The metastable-state topology shows high connectivity and low modularity in two regions of the ?,? plane for any effective infection rate ?>?c: (i) a “strongly adaptive” region with very high ? and (ii) a “weakly adaptive” region with very low ?. These two regions are separated from the other half-open elliptical-like regions of low connectivity and high modularity in a contour-line-like way. Our results indicate that the adaptation of the topology in response to disease dynamics suppresses the infection, while it promotes the network evolution towards a topology that exhibits assortative mixing, modularity, and a binomial-like degree distribution.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Coronary Aneurysm-like Dilation with Stenosis, Anti-phospholipid Antibody Syndrome, Nephrotic Syndrome, and Polycystic Kidney: A Case Report

Here we report a case that a young man had early onset myocardial infarction. Coronary angiography showed coronary aneurysm-like dilation and thromboembolism. After stents were implanted, his condition was complicated with repeated stent restenosis. Polycystic kidney, nephrotic syndrome and antiphospholipid antibody syndrome were also present. Antiphospholipid antibody syndrome, a risk factor for recurrent coronary thrombosis, can lead to nephrotic syndrome. Polycystic kidney can be characterized by nephrotic syndrome and may be combined with aneurysmal lesions due to genetic abnormalities. According to the multidisciplinary discussion and follow-up results, the patient was diagnosed as connective tissue diseases and secondary anti-phospholipid antibody syndrome, nephrotic syndrome, and coronary artery lesions. The patient's symptoms improved after treatment for the original disease. The management of this patient broadened our understanding of the etiology of coronary artery disease in young patients and demonstrated the importance of multidisciplinary clinical thinking

Five Silkworm 30K Proteins Are Involved in the Cellular Immunity against Fungi

Background: 30K proteins are a major group of nutrient storage proteins in the silkworm hemolymph. Previous studies have shown that 30K proteins are involved in the anti-fungal immunity; however, the molecular mechanism involved in this immunity remains unclear. Methods: We investigated the transcriptional expression of five 30K proteins, including BmLP1, BmLP2, BmLP3, BmLP4, and BmLP7. The five recombinant 30K proteins were expressed in an Escherichia coli expression system, and used for binding assays with fungal cells and hemocytes. Results: The transcriptional expression showed that the five 30K proteins were significantly upregulated after injection of pathogen-associated molecular patterns to the fifth instar larvae, indicating the possibility of their involvement in immune response. The binding assay showed that only BmLP1 and BmLP4 can bind to both fungal cells and silkworm hemocytes. Furthermore, we found that BmLP1-coated and BmLP4-coated agarose beads promote encapsulation of hemocytes in vitro. The hemocyte encapsulation was blocked when the BmLP1-coated beads were preincubated with BmLP1 specific polyclonal antibodies. Conclusions: These results demonstrate that 30K proteins are involved in the cellular immunity of silkworms by acting as pattern recognition molecules to directly recruit hemocytes to the fungal surface