Roles for Transcription Factors Sp1, NF-kappa B, IRF3, and IRF7 in Expression of the Human IFNL4 Gene

The expression of a biologically active human IFN4 depends on the presence of a frameshift deletion polymorphism within the first exon of the interferon lambda 4 (IFNL4) gene. In this report, we use the lung carcinoma-derived cell line, A549, which is genetically viable to express a functional IFN4, to address transcriptional requirements of the IFNL4 gene. We show that the GC-rich DNA-binding transcription factor (TF) specificity protein 1 (Sp1) is recruited to the IFNL4 promoter and has a role in induction of gene expression upon stimulation with viral RNA mimic poly(I:C). By using RNAi and overexpression strategies, we also show key roles in IFNL4 gene expression for the virus-inducible TFs, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-B), IFN regulatory factor 3 (IRF3), and IRF7. Interestingly, we also observe that overexpression of IFN4 influences IFNL4 promoter activity, which may further be dependent on the retinoic acid-inducible gene-I (RIG-I)-like receptor pathway. Together, our work for the first time reports on the functional characterization of the human IFNL4 promoter.

Autoregulation of topoisomerase I expression by supercoiling sensitive transcription

The opposing catalytic activities of topoisomerase I (TopoI/relaxase) and DNA gyrase (supercoiling enzyme) ensure homeostatic maintenance of bacterial chromosome supercoiling. Earlier studies in Es-cherichia coli suggested that the alteration in DNA supercoiling affects the DNA gyrase and TopoI expression. Although, the role of DNA elements around the promoters were proposed in regulation of gyrase, the molecular mechanism of supercoiling mediated control of TopoI expression is not yet understood. Here, we describe the regulation of TopoI expression from Mycobacterium tuberculosis and Mycobac-terium smegmatis by a mechanism termed Supercoiling Sensitive Transcription (SST). In both the organisms, topoI promoter(s) exhibited reduced activity in response to chromosome relaxation suggesting that SST is intrinsic to topoI promoter(s). We elucidate the role of promoter architecture and high transcriptional activity of upstream genes in topoI regulation. Analysis of the promoter(s) revealed the presence of suboptimal spacing between the -35 and -10 elements, rendering them supercoiling sensitive. Accordingly, upon chromosome relaxation, RNA polymerase occupancy was decreased on the topoI promoter region implicating the role of DNA topology in SST of topoI. We propose that negative supercoiling induced DNA twisting/writhing align the -35 and -10 elements to facilitate the optimal transcription of topoI.

Development of the 2003 CU-HTK conversational telephone speech transcription system

This paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for Conversational Telephone Speech (CTS). The system was designed based on a multi-pass, multi-branch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques such as Speaker Adaptive Training, Heteroscedastic Linear Discriminant Analysis, Minimum Phone Error estimation and specially constructed Single Pronunciation dictionaries were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 Rich Transcription (RT-03) evaluation test set is discussed.

Unsupervised training for mandarin broadcast news and conversation transcription

A significant cost in obtaining acoustic training data is the generation of accurate transcriptions. For some sources close-caption data is available. This allows the use of lightly-supervised training techniques. However, for some sources and languages close-caption is not available. In these cases unsupervised training techniques must be used. This paper examines the use of unsupervised techniques for discriminative training. In unsupervised training automatic transcriptions from a recognition system are used for training. As these transcriptions may be errorful data selection may be useful. Two forms of selection are described, one to remove non-target language shows, the other to remove segments with low confidence. Experiments were carried out on a Mandarin transcriptions task. Two types of test data were considered, Broadcast News (BN) and Broadcast Conversations (BC). Results show that the gains from unsupervised discriminative training are highly dependent on the accuracy of the automatic transcriptions. © 2007 IEEE.

Discriminative language model adaptation for Mandarin broadcast speech transcription and translation

This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 IEEE.

Improving speech transcription for Mandarin-english translation

This paper describes the development of the CU-HTK Mandarin Speech-To-Text (STT) system and assesses its performance as part of a transcription-translation pipeline which converts broadcast Mandarin audio into English text. Recent improvements to the STT system are described and these give Character Error Rate (CER) gains of 14.3% absolute for a Broadcast Conversation (BC) task and 5.1% absolute for a Broadcast News (BN) task. The output of these STT systems is then post-processed, so that it consists of sentence-like segments, and translated into English text using a Statistical Machine Translation (SMT) system. The performance of the transcription-translation pipeline is evaluated using the Translation Edit Rate (TER) and BLEU metrics. It is shown that improving both the STT system and the post-STT segmentations can lower the TER scores by up to 5.3% absolute and increase the BLEU scores by up to 2.7% absolute. © 2007 IEEE.

The CU-HTK Mandarin broadcast news transcription system

This paper discusses the development of the CU-HTK Mandarin Broadcast News (BN) transcription system. The Mandarin BN task includes a significant amount of English data. Hence techniques have been investigated to allow the same system to handle both Mandarin and English by augmenting the Mandarin training sets with English acoustic and language model training data. A range of acoustic models were built including models based on Gaussianised features, speaker adaptive training and feature-space MPE. A multi-branch system architecture is described in which multiple acoustic model types, alternate phone sets and segmentations can be used in a system combination framework to generate the final output. The final system shows state-of-the-art performance over a range of test sets. ©2006 British Crown Copyright.

Automatic transcription of conversational telephone speech

This paper discusses the Cambridge University HTK (CU-HTK) system for the automatic transcription of conversational telephone speech. A detailed discussion of the most important techniques in front-end processing, acoustic modeling and model training, language and pronunciation modeling are presented. These include the use of conversation side based cepstral normalization, vocal tract length normalization, heteroscedastic linear discriminant analysis for feature projection, minimum phone error training and speaker adaptive training, lattice-based model adaptation, confusion network based decoding and confidence score estimation, pronunciation selection, language model interpolation, and class based language models. The transcription system developed for participation in the 2002 NIST Rich Transcription evaluations of English conversational telephone speech data is presented in detail. In this evaluation the CU-HTK system gave an overall word error rate of 23.9%, which was the best performance by a statistically significant margin. Further details on the derivation of faster systems with moderate performance degradation are discussed in the context of the 2002 CU-HTK 10 × RT conversational speech transcription system. © 2005 IEEE.