Topic Identification for Speech without ASR

Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable. In this paper, we investigate alternative unsupervised solutions to obtaining tokenizations of speech in terms of a vocabulary of automatically discovered word-like or phoneme-like units, without depending on the supervised training of ASR systems. Moreover, using automatic phoneme-like tokenizations, we demonstrate that a convolutional neural network based framework for learning spoken document representations provides competitive performance compared to a standard bag-of-words representation, as evidenced by comprehensive topic ID evaluations on both single-label and multi-label classification tasks.Comment: 5 pages, 2 figures; accepted for publication at Interspeech 201

Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR

Bubble Size and Mass Transfer in a Modified Airlift Loop Reactor with Continuous Slurry Phase

A modified internal airlift loop reactor with continuous slurry phases was explored to investigate the local bubble size and the local mass transfer properties. A mathematical model was derived to simulate the bubble size in every flow region. Also, a new method was developed to measure the dissolved oxygen concentration

Flow Field in a Novel Short Residence Time Gas-solid Separator

The gas flow field in a short residence time separator was investigated. The tangential velocity in the separator housing increases with increasing angle to the positive x axis, and decreases with increasing radial position. A swirl of opposite direction to the main current in the separator housing occurs in the gas outlet

CFD simulation of hydrodynamic characteristics in a modified internally circulating fluidized bed mixer

A modified internally circulating fluidized bed (MICFB) was proposed as a particle mixer by coupling a pre-mixing section and a modified ICFB section[1]. Four slots were opened at the upside of the draft tube to improve further particle mixing. Hydrodynamics of MICFB was numerically investigated by multi-scale simulation based on a structure–dependent EMMS model[2]. Results showed that strong particle mixing mainly occurred in three regions, the bottom region, the draft tube region and the rectangular slots affected region. At the bottom region, due to the jet and the particles circulating from the annulus, bed density and particle velocity distributed unevenly. A cross-flow occurred in this region, with the circulating particles moving horizontally and the initial bubbles rising vertically. With increasing superficial gas velocity, particle rising velocity and particle circulating mass flow rate increased, leading to better particle mixing. In the slots affected region, radial distribution of bed density seems flat and the rising velocity decreased in the draft tube, while bed density significantly increased in the annulus. Nearly 62 wt. % particles entered the gas-solid separator region and then flowed into the annulus region, while the rest particles directly circulated into the annulus through the slots. A cross-flow of particles was also observed near the slots, with particles from the gas-solid separator region moving downwards and those circulating through slots flowing horizontally. Compared with ICFB with no slots, MICFB had a greater particle circulation mass flow rate with an increase of 20%, which consequently resulted in further particle mixing. Please click Additional Files below to see the full abstract

Knockdown of astrocyte elevated gene-1 inhibits proliferation and enhancing chemo-sensitivity to cisplatin or doxorubicin in neuroblastoma cells

<p>Abstract</p> <p>Background</p> <p><it>Astrocyte elevated gene-1 </it>(<it>AEG</it>-<it>1</it>) was originally characterized as a HIV-1-inducible gene in primary human fetal astrocyte. Recent studies highlight a potential role of <it>AEG-1 </it>in promoting tumor progression and metastasis. The aim of this study was to investigate if <it>AEG-1 </it>serves as a potential therapeutic target of human neuroblastoma.</p> <p>Methods</p> <p>We employed RNA interference to reduce <it>AEG-1 </it>expression in human neuroblastoma cell lines and analyzed their phenotypic changes.</p> <p>Results</p> <p>We found that the knockdown of <it>AEG-1 </it>expression in human neuroblastoma cells significantly inhibited cell proliferation and apoptosis. The specific downregulation induced cell arrest in the G₀/G₁phase of cell cycle. In the present study, we also observed a significant enhancement of chemo-sensitivity to cisplatin and doxorubicin by knockdown of <it>AEG-1</it>.</p> <p>Conclusion</p> <p>Our study suggests that overexpressed <it>AEG-1 </it>enhance the tumorogenic properties of neuroblastoma cells. The inhibition of <it>AEG-1 </it>expression could be a new adjuvant therapy for neuroblastoma.</p

A pH-sensitive multifunctional gene carrier assembled via layer-by-layer technique for efficient gene delivery

Peng Li, Donghua Liu, Lei Miao, Chunxi Liu, Xiaoli Sun, Yongjun Liu, Na ZhangSchool of Pharmaceutical Science, Shandong University, Jinan, Shandong, People&amp;rsquo;s Republic of ChinaBackground: The success of gene therapy asks for the development of multifunctional vectors that could overcome various gene delivery barriers, such as the cell membrane, endosomal membrane, and nuclear membrane. Layer-by-layer technique is an efficient method with easy operation which can be used for the assembly of multifunctional gene carriers. This work describes a pH-sensitive multifunctional gene vector that offered long circulation property but avoided the inhibition of tumor cellular uptake of gene carriers associated with the use of polyethylene glycol.Methods: Deoxyribonucleic acid (DNA) was firstly condensed with protamine into a cationic core which was used as assembly template. Then, additional layers of anionic DNA, cationic liposomes, and o-carboxymethyl-chitosan (CMCS) were alternately adsorbed onto the template via layer-by-layer technique and finally the multifunctional vector called CMCS-cationic liposome-coated DNA/protamine/DNA complexes (CLDPD) was constructed. For in vitro test, the cytotoxicity and transfection investigation was carried out on HepG2 cell line. For in vivo evaluation, CMCS-CLDPD was intratumorally injected into tumor-bearing mice and the tumor cells were isolated for fluorescence determination of transfection efficiency.Results: CMCS-CLDPD had ellipsoidal shapes and showed &amp;ldquo;core-shell&amp;rdquo; structure which showed stabilization property in serum and effective protection of DNA from nuclease degradation. In vitro and in vivo transfection results demonstrated that CMCS-CLDPD had pH-sensitivity and the outermost layer of CMCS fell off in the tumor tissue, which could not only protect CMCS-CLDPD from serum interaction but also enhance gene transfection efficiency.Conclusion: These results demonstrated that multifunctional CMCS-CLDPD had pH-sensitivity, which may provide a new approach for the antitumor gene delivery.Keywords: layer-by-layer, multifunctional nanovector, pH-sensitivity, gene deliver

An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. AUD provides an important avenue for unsupervised acoustic model training in a zero resource setting where expert-provided linguistic knowledge and transcribed speech are unavailable. Therefore, to further facilitate zero-resource AUD process, in this paper, we demonstrate acoustic feature representations can be significantly improved by (i) performing linear discriminant analysis (LDA) in an unsupervised self-trained fashion, and (ii) leveraging resources of other languages through building a multilingual bottleneck (BN) feature extractor to give effective cross-lingual generalization. Moreover, we perform comprehensive evaluations of AUD efficacy on multiple downstream speech applications, and their correlated performance suggests that AUD evaluations are feasible using different alternative language resources when only a subset of these evaluation resources can be available in typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201