Attention-Based End-to-End Speech Recognition on Voice Search

Recently, there has been a growing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. In this paper, we explore the use of attention-based encoder-decoder model for Mandarin speech recognition on a voice search task. Previous attempts have shown that applying attention-based encoder-decoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model. In this paper, we use character embedding to deal with the large vocabulary. Several tricks are used for effective model training, including L2 regularization, Gaussian weight noise and frame skipping. We compare two attention mechanisms and use attention smoothing to cover long context in the attention model. Taken together, these tricks allow us to finally achieve a character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on the MiTV voice search dataset. While together with a trigram language model, CER and SER reach 2.81% and 5.77%, respectively

Empirical Evaluation of Speaker Adaptation on DNN based Acoustic Model

Speaker adaptation aims to estimate a speaker specific acoustic model from a speaker independent one to minimize the mismatch between the training and testing conditions arisen from speaker variabilities. A variety of neural network adaptation methods have been proposed since deep learning models have become the main stream. But there still lacks an experimental comparison between different methods, especially when DNN-based acoustic models have been advanced greatly. In this paper, we aim to close this gap by providing an empirical evaluation of three typical speaker adaptation methods: LIN, LHUC and KLD. Adaptation experiments, with different size of adaptation data, are conducted on a strong TDNN-LSTM acoustic model. More challengingly, here, the source and target we are concerned with are standard Mandarin speaker model and accented Mandarin speaker model. We compare the performances of different methods and their combinations. Speaker adaptation performance is also examined by speaker's accent degree.Comment: Interspeech 201

Small signal stability analysis for different types of PMSGs connected to the grid

Small signal stability of permanent magnet synchronous generator (PMSG)-based wind turbines connected to the power grid should be studied properly in order to facilitate damping strategy design. In this paper, unified small-signal models for different types of PMSGs are developed to study their small-signal stability. The models are composed of mechanical systems, electrical systems and control systems. A two-mass shaft model for the mechanical system is provided to analyze the dynamic and steady-state behaviors of the wind turbine and generator rotor. Meanwhile, PMSG, converter system and transmission line are separately modeled to build unified small-signal models for three PMSG-based wind turbine generator systems (WTGS). Then, based on unified small-signal models, eigenvalue analysis is conducted to determine the relation between different oscillation modes and state variables through calculating participation factors. With modal analysis, the developed small signal models are able to find out all types of oscillation modes for PMSGs connected to the power grid, which are subsynchronous oscillation (SSO), subsynchronous control interaction (SSCI) and low-frequency oscillation, including frequency and damping of each oscillation mode. Different initial values of the small signal models can influence both frequencies and damping ratios of oscillation modes, which lay basis for further damping strategy study.</p

Hormona Paratiróideia Como Factor Predictivo de Hipocalcemia Após Tiroidectomia: Estudo Prospectivo em 100 Doentes

INTRODUCTION: Hypocalcemia is a frequent complication after total thyroidectomy and the main reason for prolonged hospitalization of these patients. MATERIAL AND METHODS: We studied prospectively 112 patients who underwent total or completation thyroidectomy between June 2012 and November 2013. Twelve patients with preoperative changes in parathyroid function were excluded. Parathyroid hormone and calcium levels were determined pre-operatively, immediately after surgery, on 1st day and on 14th day after surgery. RESULTS: Of the 100 patients enrolled, 60 have developed hypocalcaemia (60%) but only 14 patients had symptomatic hypocalcaemia. It mostly occurs 24 hours after surgery (76.7%). It was permanent in 3 patients and temporary in the others. In the 60 patients with hypocalcaemia, it has been found hypoparathyroidism in 19 patients immediately after surgery, in 14 patients on 1st day but only 3 had hypoparathyroidism (patients with permanent hypocalcaemia). Comparing the group of patients with and without hypocalcaemia we found a decrease of parathyroid hormone in both (immediately after surgery and on 1st day) but was more important in the hypocalcaemia group (p = 0.004 and p 19.4% determined on the 1st day (sensitivity = 82%; specificity = 63%). DISCUSSION: In our study there was a high incidence of hypocalcemia (60%), expressed predominantly 24 hours after surgery and conditioned, in these patients, a longer hospital stay. However, only 3 patients (3%) had permanent hypocalcemia. We still found a match in the oscillation of serum calcium levels and parathyroid hormone which identified the decrease in parathyroid hormone on the first day after surgery as a reliable predictor of hypocalcemia. CONCLUSION: Decrease of parathyroid hormone levels > 19.4% determined on 1st day is a good predictor of hypocalcemia after total / completation thyroidectomy, allowing to identify patients at higher risk of hypocalcemia, medicate them prophylactically and get early and safe discharges.info:eu-repo/semantics/publishedVersio

Mathematical model of two-degree-of-freedom direct drive induction motor considering coupling effect

The Two-degree-of-freedom direct drive induction motor, which is capable of linear, rotary and helical two, has a wide application in special industry such as industrial robot arms. It is inevitable that the linear motion and rotary motion generate coupling effect on each other on account of the high integration. The analysis of this effect has great significance in the research of two-degree-of-freedom motors, which is also crucial to realize precision control of them. The coupling factor considering the coupling effect is proposed and addressed by 3D finite element method. Then the corrected mathematical model is presented by importing the coupling factor. The results from it are verified by 3D finite element model and prototype test, which validates the corrected mathematical model

Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition

We investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. GANs have been recently studied for speech enhancement to remove additive noises, but there still lacks of a work to examine their ability in speech dereverberation and the advantages of using GANs have not been fully established. In this paper, we provide deep investigations in the use of GAN-based dereverberation front-end in ASR. First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset. Second, further adding residual connections in the deep LSTMs can boost the performance as well. Finally, we find that, for the success of GAN, it is important to update the generator and the discriminator using the same mini-batch data during training. Moreover, using reverberant spectrogram as a condition to discriminator, as suggested in previous studies, may degrade the performance. In summary, our GAN-based dereverberation front-end achieves 14%-19% relative CER reduction as compared to the baseline DNN dereverberation network when tested on a strong multi-condition training acoustic model.Comment: Interspeech 201

A Galactomannoglucan Derived from Agaricus brasiliensis: Purification, Characterization and Macrophage Activation via MAPK and IkappaB/NFkappaB Pathways

In this study, a novel galactomannoglucan named as TJ2 was isolated from Agaricus brasiliensis with microwave extraction, macroporous resin, ion exchange resin and high resolution gel chromatography. TJ2 is composed of glucose, mannose and galactose in the ratio 99.2:0.2:0.6. Infrared spectra (IR), methylation analysis and nuclear magnetic resonance spectra indicated that TJ2 mainly contained a b-(1?3) – linked glucopyranosyl backbone. Interestingly, TJ2 significantly promoted RAW264.7 cell proliferation, and was able to activate the cells to engulf E. coli. In addition, TJ2 induced the expression of Interleukin 1b (IL-1b), Interleukin 6 (IL-6), tumor necrosis factor a (TNF-a) and cyclooxygenase-2 (Cox-2) in the cells. TJ2 also promoted the production of nitric oxide (NO) by inducing the expression of inducible nitric oxide synthase (iNOS). Moreover, TJ2 is a potent inducer in activating the mitogen-activated protein kinase (MAPK) and inhibitor of nuclear factor-kappa B (IkappaB)/nuclear factor-kappa B (NFkappaB) pathways