Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis

This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences. Only the alignment paths that satisfy the monotonic condition are taken into consideration at each decoder timestep. The modified attention probabilities at each timestep are computed recursively using a forward algorithm. A transition agent for forward attention is further proposed, which helps the attention mechanism to make decisions whether to move forward or stay at each decoder timestep. Experimental results show that the proposed forward attention method achieves faster convergence speed and higher stability than the baseline attention method. Besides, the method of forward attention with transition agent can also help improve the naturalness of synthetic speech and control the speed of synthetic speech effectively.Comment: 5 pages, 3 figures, 2 tables. Published in IEEE International Conference on Acoustics, Speech and Signal Processing 2018 (ICASSP2018

Listening and grouping: an online autoregressive approach for monaural speech separation

This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation. It exploits a causal temporal context in both mixture and past estimated separated signals and performs online separation that is compatible with real-time applications. The approach adopts a learned listening and grouping architecture motivated by computational auditory scene analysis, with a grouping stage that effectively addresses the label permutation problem at both frame and segment levels. Experimental results on the benchmark WSJ0-2mix dataset show that the new approach can outperform the majority of state-of-the-art methods in both closed-set and open-set conditions in terms of signal-to-distortion ratio (SDR) improvement and perceptual evaluation of speech quality (PESQ), even approaches that exploit whole-utterance statistics for separation, with relatively fewer model parameters

Role of the porous structure of the bioceramic scaffolds in bone tissue engineering

The porous structure of biomaterials plays a critical role in improving the efficiency of biomaterials in tissue engineering. Here we fabricate successfully porous bioceramics with accurately controlled pore parameters, and investigate the effect of pore parameters on the mechanical property, the cell seeding proliferation and the vascularization of the scaffolds. This study shows that the porosity play an important role on the mechanical property of the scaffolds, which is affected not only by the macropores size, but also by the interconnections of the scaffolds. Larger pores are beneficial for cell growth in scaffolds. In contrast, the interconnections do not affect cell growth much. The interconnections appear to limit the number of blood vessels penatrating through adjacent pores, and both the pores size and interconnections can determine the size of blood vessels. The results may be referenced on the selective design of porous structure of biomaterials to meet the specificity of biological application

Luteoloside Inhibits Proliferation of Human Chronic Myeloid Leukemia K562 Cells by Inducing G2/M Phase Cell Cycle Arrest and Apoptosis

Purpose: To investigate the effects of luteoloside on the proliferation of human chronic myeloid leukemia K562 cells and whether luteoloside induces cell cycle arrest and apoptosis in K562 cells.Methods: Luteoloside’s cytotoxicity was assessed using a cell counting kit. Cell cycle distribution was analysed by flow cytometry after propidium iodide (PI) staining. Cell apoptosis was assayed with apoptosis detection kit and Hoechst staining followed by observation under a fluorescence microscope. The expression of cell cycle- and apoptosis-related proteins was examined by Western blot analysis.Results: Luteoloside inhibited the proliferation of K562 cells in a dose- and time- dependent manner (IC50 = 30.7 μM) with less toxicity in a normal human cell line (IC50 = 91.8 μM). Moreover, antiproliferative effect of luteoloside was accompanied with G2/M phase arrest（p ＜ 0.05 or p＜0.01） and apoptosis（p ＜ 0.01 or p ＜ 0.001）. Further studies revealed that the expression level of cyclinB1 was down-regulated by luteoloside treatment. Furthermore, luteoloside treatment also increased proapoptotic protein Bax expression and decreased anti-apoptotic protein Bcl-2 expression.Conclusion: These results suggest that the inhibitory effect of luteoloside on K562 cell proliferation is associated with inducing G2/M phase arrest and apoptosis, and that luteoloside is worth further studying for anticancer potential.Keywords: Luteoloside, Myeloid leukemia, Proliferation, Cell cycle arrest, Apoptosis, Anticance

Improvements on Deep Bottleneck Network based I-Vector Representation for Spoken Language Identification

Recently, the i-vector representation based on deep bottleneck networks (DBN) pre-trained for automatic speech recognition has received significant interest for both speaker verification (SV) and language identification (LID). In particular, a recent unified DBN based i-vector framework, referred to as DBN-pGMM i-vector, has performed well. In this paper, we replace the pGMM with a phonetic mixture of factor analyzers (pMFA), and propose a new DBN-pMFA i-vector. The DBN-pMFA i-vector includes the following improvements: (i) a pMFA model is derived from the DBN, which can jointly perform feature dimension reduction and de-correlation in a single linear transformation, (ii) a shifted DBF, termed SDBF, is proposed to exploit the temporal contextual information, (iii) a senone selection scheme is proposed to improve the i-vector extraction efficiently. We evaluate the proposed DBN-pMFA i-vector on the most confused six languages selected from NIST LRE 2009. The experimental results demonstrate that DBN-pMFA can consistently outperform the previous DBN based framework. The computational complexity can be significantly reduced by applying a simple senone selection scheme