Contrastive Speaker Embedding With Sequential Disentanglement

Contrastive speaker embedding assumes that the contrast between the positive and negative pairs of speech segments is attributed to speaker identity only. However, this assumption is incorrect because speech signals contain not only speaker identity but also linguistic content. In this paper, we propose a contrastive learning framework with sequential disentanglement to remove linguistic content by incorporating a disentangled sequential variational autoencoder (DSVAE) into the conventional SimCLR framework. The DSVAE aims to disentangle speaker factors from content factors in an embedding space so that only the speaker factors are used for constructing a contrastive loss objective. Because content factors have been removed from the contrastive learning, the resulting speaker embeddings will be content-invariant. Experimental results on VoxCeleb1-test show that the proposed method consistently outperforms SimCLR. This suggests that applying sequential disentanglement is beneficial to learning speaker-discriminative embeddings.Comment: Submitted to ICASSP 202

Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification

Contrastive self-supervised learning (CSL) for speaker verification (SV) has drawn increasing interest recently due to its ability to exploit unlabeled data. Performing data augmentation on raw waveforms, such as adding noise or reverberation, plays a pivotal role in achieving promising results in SV. Data augmentation, however, demands meticulous calibration to ensure intact speaker-specific information, which is difficult to achieve without speaker labels. To address this issue, we introduce a novel framework by incorporating clean and augmented segments into the contrastive training pipeline. The clean segments are repurposed to pair with noisy segments to form additional positive and negative pairs. Moreover, the contrastive loss is weighted to increase the difference between the clean and augmented embeddings of different speakers. Experimental results on Voxceleb1 suggest that the proposed framework can achieve a remarkable 19% improvement over the conventional methods, and it surpasses many existing state-of-the-art techniques.Comment: 5 pages, 2 figures, submitted to ICASSP 202

Deep reinforcement learning for automated radiation adaptation in lung cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/1/mp12625.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/2/mp12625_am.pd

Use of Chinese Herbal Medicine Was Related to Lower Risk of Osteoporotic Fracture in Sarcopenia Patients: Evidence from Population-Based Health Claims

Introduction: With population aging, sarcopenia and its accompanying risk of osteoporotic fracture has drawn increased attention. Nowadays, while Chinese herbal medicine (CHM) is often used as complementary therapy for many medical conditions, its effect against likelihood of osteoporotic fracture among sarcopenia subjects was not fully elucidated yet. We therefore conducted a population-level study to compare osteoporotic fracture risk for sarcopenia persons with or without CHM use. Methods: Using the patient record from a nationwide insurance database, we recruited persons with newly diagnosed sarcopenia and simultaneously free of osteoporotic fracture between 2000 and 2010. Propensity score matching was then applied to randomly select sets of CHM users and non-CHM users. All of them were tracked until end of 2013 to measure the incidence and adjusted hazard ratios (HRs) for new new-onset fracture in multivariable Cox proportional hazards model. Results: Compared to non-CHM users, the CHM users indeed had a lower incidence of osteoporotic fracture (121.22 vs 156.61 per 1000 person-years). Use of CHM correlated significantly with a lower fracture likelihood after adjusting for potential covariates, and those receiving CHM treatment for more than two years experienced a remarkably lower risk by 73%. Uses of several herbal formulae were correlated to reduced risk of osteoporotic fracture, such as Caulis Spatholobi, Xuduan, Duzhong, Danshen, Shu-Jing-Huo-Xue- Tang, Du-Huo-Ji-Sheng-Tang, Shao-Yao-Gan-Cao-Tang, and Shen-Tong-Zhu-Yu -Tang. Conclusion: Our study depicted that cumulative CHM exposure was inversely associated with osteoporotic fracture risk in a duration-dependent manner, implying that CHM treatment may be embraced as routine care in preventing incident osteoporotic fracture