Distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms

Recent strides in neural speech synthesis technologies, while enjoying widespread applications, have nonetheless introduced a series of challenges, spurring interest in the defence against the threat of misuse and abuse. Notably, source attribution of synthesized speech has value in forensics and intellectual property protection, but prior work in this area has certain limitations in scope. To address the gaps, we present our findings concerning the identification of the sources of synthesized speech in this paper. We investigate the existence of speech synthesis model fingerprints in the generated speech waveforms, with a focus on the acoustic model and the vocoder, and study the influence of each component on the fingerprint in the overall speech waveforms. Our research, conducted using the multi-speaker LibriTTS dataset, demonstrates two key insights: (1) vocoders and acoustic models impart distinct, model-specific fingerprints on the waveforms they generate, and (2) vocoder fingerprints are the more dominant of the two, and may mask the fingerprints from the acoustic model. These findings strongly suggest the existence of model-specific fingerprints for both the acoustic model and the vocoder, highlighting their potential utility in source identification applications.Comment: Submitted to ICASSP 202

Effects of additional food availability and pulse control on the dynamics of a Holling-(p+1) type pest-natural enemy model

In this paper, a novel pest-natural enemy model with additional food source and Holling-($p$+1) type functional response is put forward for plant pest management by considering multiple food sources for predators. The dynamical properties of the model are investigated, including existence and local asymptotic stability of equilibria, as well as the existence of limit cycles. The inhibition of natural enemy on pest dispersal and the impact of additional food sources on system dynamics are elucidated. In view of the fact that the inhibitory effect of the natural enemy on pest dispersal is slow and in general deviated from the expected target, an integrated pest management model is established by regularly releasing natural enemies and spraying insecticide to improve the control effect. The influence of the control period on the global stability and system persistence of the pest extinction periodic solution is discussed. It is shown that there exists a time threshold, and as long as the control period does not exceed that threshold, pests can be completely eliminated. When the control period exceeds that threshold, the system can bifurcate the supercritical coexistence periodic solution from the pest extinction one. To illustrate the main results and verify the effectiveness of the control method, numerical simulations are implemented in MATLAB programs. This study not only enriched the related content of population dynamics, but also provided certain reference for the management of plant pest

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks

Heat shock protein 90 promotes RNA helicase DDX5 accumulation and exacerbates hepatocellular carcinoma by inhibiting autophagy

Objective: Hepatocellular carcinoma (HCC), the main type of liver cancer, has a high morbidity and mortality, and a poor prognosis. RNA helicase DDX5, which acts as a transcriptional co-regulator, is overexpressed in most malignant tumors and promotes cancer cell growth. Heat shock protein 90 (HSP90) is an important molecular chaperone in the conformational maturation and stabilization of numerous proteins involved in cell growth or survival. Methods: DDX5 mRNA and protein expression in surgically resected HCC tissues from 24 Asian patients were detected by quantitative real-time PCR and Western blot, respectively. The interaction of DDX5-HSP90 was determined by molecular docking, immunoprecipitation, and laser scanning confocal microscopy. The autophagy signal was detected by Western blot. The cell functions and signaling pathways of DDX5 were determined in 2 HCC cell lines. Two different murine HCC xenograft models were used to determine the function of DDX5 and the therapeutic effect of an HSP90 inhibitor. Results: HSP90 interacted directly with DDX5 and inhibited DDX5 protein degradation in the AMPK/ULK1-regulated autophagy pathway. The subsequent accumulation of DDX5 protein induced the malignant phenotype of HCC by activating the β-catenin signaling pathway. The silencing of DDX5 or treatment with HSP90 inhibitor both blocked in vivo tumor growth in a murine HCC xenograft model. High levels of HSP90 and DDX5 protein were associated with poor prognoses. Conclusions: HSP90 interacted with DDX5 protein and subsequently protected DDX5 protein from AMPK/ULK1-regulated autophagic degradation. DDX5 and HSP90 are therefore potential therapeutic targets for HCC

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery