Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations

This paper aims to enhance low-resource TTS by reducing training data requirements using compact speech representations. A Multi-Stage Multi-Codebook (MSMC) VQ-GAN is trained to learn the representation, MSMCR, and decode it to waveforms. Subsequently, we train the multi-stage predictor to predict MSMCRs from the text for TTS synthesis. Moreover, we optimize the training strategy by leveraging more audio to learn MSMCRs better for low-resource languages. It selects audio from other languages using speaker similarity metric to augment the training set, and applies transfer learning to improve training quality. In MOS tests, the proposed system significantly outperforms FastSpeech and VITS in standard and low-resource scenarios, showing lower data requirements. The proposed training strategy effectively enhances MSMCRs on waveform reconstruction. It improves TTS performance further, which wins 77% votes in the preference test for the low-resource TTS with only 15 minutes of paired data.Comment: Submitted to ICASSP 202

Will savings from biosimilars offset increased costs related to dose escalation? A comparison of infliximab and golimumab for rheumatoid arthritis

INTRODUCTION: Biosimilar infliximab has the potential for appreciable cost savings compared to its reference biologic, but dose escalation is common and increases costs. We compared frequency of dose escalation and associated Medicare-approved amount so as to determine the break-even point at which infliximab dose escalation would offset the cost savings of using a biosimilar, referent to alternatively using golimumab. METHODS: We studied Medicare enrollees with rheumatoid arthritis (RA) initiating infliximab or golimumab. Frequency of dose escalation was summarized descriptively over 18 months, as were Medicare-approved amounts for reimbursement. Analyses were repeated conditioning on high adherence (i.e., non-discontinuation, \u3e 10-week gap). Multivariable-adjusted logistic regression and mixed models evaluated factors associated with infliximab dose escalation. RESULTS: A total of 5174 infliximab and 2843 golimumab initiators were observed. Dose escalation was rare for golimumab (5%) but common for infliximab (49%), and was even more common (72%) for infliximab among patients who persisted on treatment. Regardless of dose escalation, the adjusted least square mean dollar amounts were appreciably higher for golimumab ($28,146) than for infliximab ($21,216) and greater among persistent patients (cost difference $9269, favoring infliximab). Only when patients escalated infliximab to \u3e /= 8 mg/kg every 6 weeks was golimumab IV at break-even or less expensive. After controlling for multiple factors, physician ownership of the infusion center was associated with greater likelihood of infliximab dose escalation (odds ratio = 1.25, 95% CI 1.09-1.44). CONCLUSION: Despite frequent dose escalation with infliximab that often increase its dose by threefold or more, the savings from the current price of its biosimilar substantially offsets the costs of an alternative infused TNFi biologic for which no biosimilar is available

A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS

We propose a Multi-Stage, Multi-Codebook (MSMC) approach to high-performance neural TTS synthesis. A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is used to encode Mel spectrograms of speech training data by down-sampling progressively in multiple stages into MSMC Representations (MSMCRs) with different time resolutions, and quantizing them with multiple VQ codebooks, respectively. Multi-stage predictors are trained to map the input text sequence to MSMCRs progressively by minimizing a combined loss of the reconstruction Mean Square Error (MSE) and "triplet loss". In synthesis, the neural vocoder converts the predicted MSMCRs into final speech waveforms. The proposed approach is trained and tested with an English TTS database of 16 hours by a female speaker. The proposed TTS achieves an MOS score of 4.41, which outperforms the baseline with an MOS of 3.62. Compact versions of the proposed TTS with much less parameters can still preserve high MOS scores. Ablation studies show that both multiple stages and multiple codebooks are effective for achieving high TTS performance

QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning

This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Representation Learning (VQ-S3RL) utilizing more unlabeled speech audio. This framework comprises two VQ-S3R learners: first, the principal learner aims to provide a generative Multi-Stage Multi-Codebook (MSMC) VQ-S3R via the MSMC-VQ-GAN combined with the contrastive S3RL, while decoding it back to the high-quality audio; then, the associate learner further abstracts the MSMC representation into a highly-compact VQ representation through a VQ-VAE. These two generative VQ-S3R learners provide profitable speech representations and pre-trained models for TTS, significantly improving synthesis quality with the lower requirement for supervised data. QS-TTS is evaluated comprehensively under various scenarios via subjective and objective tests in experiments. The results powerfully demonstrate the superior performance of QS-TTS, winning the highest MOS over supervised or semi-supervised baseline TTS approaches, especially in low-resource scenarios. Moreover, comparing various speech representations and transfer learning methods in TTS further validates the notable improvement of the proposed VQ-S3RL to TTS, showing the best audio quality and intelligibility metrics. The trend of slower decay in the synthesis quality of QS-TTS with decreasing supervised data further highlights its lower requirements for supervised data, indicating its great potential in low-resource scenarios

Assessing the Comparative Effect of Tocilizumab on Risk of Cardiovascular Disease among Rheumatoid Arthritis Patients Using Claims Data: A Direct Comparison among Biologic Disease-Modifying Antirheumatic Drugs

Tocilizumab is a humanized monoclonal anti-body against the interleukin-6 receptor and is effective in the treatment of rheumatoid arthritis (RA). Multiple studies have observed unfavorable changes in the lipid profile of tocilizumab-treated RA patients. Few studies have compared the cardiovascular disease (CVD) risk associated with tocilizumab to other biologic disease-modifying antirheumatic drugs (bDMARDS). Due to limitations in existing studies, the real-world association of tocilizumab with CVD risk remains uncertain. We conducted a retrospective cohort study using 2006–2015 Medicare and MarketScan claims data to assess the comparative effect of tocilizumab on CVD risk. Medicare claims data provide a unique opportunity to estimate disease burden and conduct comparative effectiveness studies with much larger sample sizes than cohorts, which require primary data collection. However, Medicare claims data lack information on cause of death. To address this limitation, we developed claims-based algorithms for identifying fatal CVD in Medicare claims data using The Reasons for Geographic and Racial Difference in Stroke (REGARDS) data linked to Medicare claims. CVD events iv and cause of death in the REGARDS study were adjudicated by two experts. Our algorithm can identify fatal CVD events with high sensitivity and specificity. Our study confirmed that tocilizumab was not associated with increased or decreased CVD risk compared to etanercept: the hazard ratio for tocilizumab compared to etanercept was 0.91 (95%CI: 0.66, 1.25). However, unlike the clinical trial, which enrolled only high-risk patients, we extended this finding to “low CVD risk” RA patients. We also showed that tocilizumab was not associated with increased or decreased CVD risk compared to abatacept or rituximab. We further showed that tocilizumab was associated with reduced CVD risk when compared to a pooled TNFi group. This is most likely due to slightly increased CVD risk associated with infliximab. We also conducted a retrospective cohort study using Medicare claims data to assess the effect of methotrexate on CVD risk among bDMARDS users and found that methotrexate was associated with an overall 27% (95%CI: 18–35%) reduction in CVD risk. The hazard ratio for tocilizumab concomitantly with methotrexate compared to tocilizumab only was 0.66 (95%CI: 0.40–1.09)

Hyphalus shiyuensis sp. nov. from Xisha Islands, China (Coleoptera, Limnichidae, Hyphalinae)

Hyphalus shiyuensis sp. nov. is described from Xisha Islands of China, which represents the ninth species and provides new distribution information for this unique intertidal genus. Brief comparisons between the new species and the known species are given. An updated key to the species of genus Hyphalus is provided