Phone-aware Neural Language Identification

Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlooked by most of existing neural LID models, although this information has been used in the conventional phonetic LID systems with a great success. We present a phone-aware neural LID architecture, which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR system. By utilizing the phonetic knowledge, the LID performance can be significantly improved. Interestingly, even if the test language is not involved in the ASR training, the phonetic knowledge still presents a large contribution. Our experiments conducted on four languages within the Babel corpus demonstrated that the phone-aware approach is highly effective.Comment: arXiv admin note: text overlap with arXiv:1705.0315

Deep Speaker Feature Learning for Text-independent Speaker Verification

Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.Comment: deep neural networks, speaker verification, speaker featur

Deep factorization for speech signal

Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individual informative factors, though it turns out to be highly difficult. Recently, we found that speaker traits, which were assumed to be long-term distributional properties, are actually short-time patterns, and can be learned by a carefully designed deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that will be presented in this paper. The proposed framework infers speech factors in a sequential way, where factors previously inferred are used as conditional variables when inferring other factors. We will show that this approach can effectively factorize speech signals, and using these factors, the original speech spectrum can be recovered with a high accuracy. This factorization and reconstruction approach provides potential values for many speech processing tasks, e.g., speaker recognition and emotion recognition, as will be demonstrated in the paper.Comment: Accepted by ICASSP 2018. arXiv admin note: substantial text overlap with arXiv:1706.0177

A new strategy for better genome assembly from very short reads

<p>Abstract</p> <p>Background</p> <p>With the rapid development of the next generation sequencing (NGS) technology, large quantities of genome sequencing data have been generated. Because of repetitive regions of genomes and some other factors, assembly of very short reads is still a challenging issue.</p> <p>Results</p> <p>A novel strategy for improving genome assembly from very short reads is proposed. It can increase accuracies of assemblies by integrating <it>de novo </it>contigs, and produce comparative contigs by allowing multiple references without limiting to genomes of closely related strains. Comparative contigs are used to scaffold <it>de novo </it>contigs. Using simulated and real datasets, it is shown that our strategy can effectively improve qualities of assemblies of isolated microbial genomes and metagenomes.</p> <p>Conclusions</p> <p>With more and more reference genomes available, our strategy will be useful to improve qualities of genome assemblies from very short reads. Some scripts are provided to make our strategy applicable at <url>http://code.google.com/p/cd-hybrid/</url>.</p

Using potassium catalytic gasification to improve the performance of solid oxide direct carbon fuel cells: Experimental characterization and elementary reaction modeling

The performance of a solid oxide electrolyte direct carbon fuel cell (SO-DCFC) is limited by the slow carbon gasification kinetics at the typical operating temperatures of cell: 650–850 °C. To overcome such limitation, potassium salt is used as a catalyst to speed up the dry carbon gasification reactions, increasing the power density by five-fold at 700–850 °C. The cell performance is shown to be sensitive to the bed temperature, emphasizing the role of gasification rates and that of CO production. Given the finite bed size, the cell performance is time-dependent as the amount of CO available changes. A reduced elementary reaction mechanism for potassium-catalyzed carbon gasification was proposed using kinetic data obtained from the experimental measurements. A comprehensive model including the catalytic gasification reactions and CO electrochemistry is used to examine the impact of the catalytic carbon gasification process on the device performance. The power density is maximum around 50% of the OCV, where carbon utilization is also near maximum. Results show that bed height and porosity impact the power density; a thicker bed maintains the power almost constant for longer times while lower porosity delivers higher power density in the early stages.National Natural Science Foundation (China) (20776078)National Natural Science Foundation (China) (51106085)Low Carbon Energy University Alliance (LCEUA) (Seed Funding