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Cross-lingual and multi-stream posterior features for low resource LVCSR systems

Cross-lingual and multi-stream posterior features for low re...

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作者： Thomas, Samuel Ganapathy, Sriram Hermansky, Hynek Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States

We investigate approaches for large vocabulary continuous speech recognition (LVCSR) system for new languages or new domains using limited amounts of transcribed training data. In these low resource conditions, the performance of conventional LVCSR systems degrade significantly. We propose to train low resource LVCSR system with additional sources of information like annotated data from other languages (German and Spanish) and various acoustic feature streams (short-term and modulation features). We train multilayer perceptrons (MLPs) on these sources of information and use Tandem features derived from the MLPs for low resource LVCSR. In our experiments, the proposed system trained using only one hour of English conversational telephone speech (CTS) provides a relative improvement of 11% over the baseline system. © 2010 ISCA.

关键词： Speech communication

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LEARNING DEEP RHETORICAL STRUCTURE FOR EXTRACTIVE SPEECH SUMMARIZATION

LEARNING DEEP RHETORICAL STRUCTURE FOR EXTRACTIVE SPEECH SUM...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Justin Jian Zhang Pascale Fung Human Language Technology Center Department of Electronic & Computer Engineering Hong Kong University of Science & Technology (HKUST) Clear Water Bay Hong Kong

ISBN: (纸本)9781424442959

Extractive summarization of conference and lecture speech is useful for online learning and references. We show for the first time that deep(er) rhetorical parsing of conference speech is possible and helpful to extractive summarization task. This type of rhetorical structures is evident in the corresponding presentation slide structures. We propose using Hidden Markov SVM (HMSVM) to iteratively learn the rhetorical structure of the speeches and summarize them. We show that system based on HMSVM gives a 64.3percent ROUGE-L F-measure, a 10.1percent absolute increase in lecture speech summarization performance compared with the baseline system without rhetorical information. Our method equally outperforms the baseline with a conventional discourse feature. Our proposed approach is more efficient than and also improves upon a previous method of using shallow rhetorical structure parsing [1].

关键词： Rhetorical structure Lecture speech summarization

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Towards long-range prosodic attribute modeling for language recognition

Towards long-range prosodic attribute modeling for language ...

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作者： Ng, Raymond W.M. Leung, Cheung-Chi Hautamäki, Ville Lee, Tan Ma, Bin Li, Haizhou Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Hong Kong Human Language Technology Department Institute for Infocomm Research A-STAR Singapore 138632 Singapore Department of Computer Science and Statistics University of Eastern Finland Finland

As a high-level feature, prosody may be an effective feature when it is modeled over longer ranges than the typical range of a syllable. This paper is about language recognition with the high-level prosodic attributes. It studies two important issues of long-range modeling, namely the data scarcity handling method, and the model which properly describes prosodic boundary events. Illustrated by NIST language recognition evaluation (LRE) 2009, long-range modeling is shown to bring a 7.2% relative improvement to a prosodic language detector. Score fusion between the long-range prosodic system and a phonotactic system gives an EER of 3.07%. Exploiting boundary N-grams is the main contributing factor to global EER reduction, while different long-range prosodic modeling factors benefit the detection of different languages. Analysis reveals the evidence of language-specific long-range prosodic attributes, which sheds light on robust long-range modeling methods for language recognition. © 2010 ISCA.

关键词： Modeling languages

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Semantic roles for SMT: A hybrid two-pass model

Semantic roles for SMT: A hybrid two-pass model

引用

2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2009

作者： Wu, Dekai Fung, Pascale Human Language Technology Center HKUST Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong Hong Kong Human Language Technology Center HKUST Department of Electronic and Computer Engineering University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781932432428

We present results on a novel hybrid semantic SMT model that incorporates the strengths of both semantic role labeling and phrase-based statistical machine translation. The approach avoids major complexity limitations via a two-pass architecture. The first pass is performed using a conventional phrase-based SMT model. The second pass is performed by a re-ordering strategy guided by shallow semantic parsers that produce both semantic frame and role labels. Evaluation on a Wall Street Journal newswire genre test set showed the hybrid model to yield an improvement of roughly half a point in BLEU score over a strong pure phrase-based SMT baseline – to our knowledge, the first successful application of semantic role labeling to SMT. © 2009 Association for Computational Linguistics

关键词： Semantics

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Can semantic role labeling improve SMT?

Can semantic role labeling improve SMT?

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13th Annual Conference of the European Association for Machine Translation, EAMT 2009

We present a series of empirical studies aimed at illuminating more precisely the likely contribution of semantic roles in improving statistical machine translation accuracy. The experiments reported study several aspects key to success: (1) the frequencies of types of SMT errors where semantic parsing and role labeling could help, and (2) if and where semantic roles offer more accurate guidance to SMT than merely syntactic annotation, and (3) the potential quantitative impact of realistic semantic role guidance to SMT systems, in terms of BLEU and METEOR scores. © 2009 European Association for Machine Translation.

关键词： computer aided language translation

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MDCT for encoding residual signals in frequency domain linear prediction

MDCT for encoding residual signals in frequency domain linea...

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127th Audio engineering Society Convention 2009

作者： Ganapathy, Sriram Motlicek, Petr Hermansky, Hynek Department of Electrical and Computer Engineering Human Language Technology Center of Excellence Johns Hopkins University United States Idiap Research Institute Martigny Switzerland

ISBN: (纸本)9781615677122

Frequency domain linear prediction (FDLP) uses autoregressive models to represent Hilbert envelopes of relatively long segments of speech/audio signals. Although the basic FDLP audio codec achieves good quality of the reconstructed signal at high bit-rates, there is a need for scaling to lower bit-rates without degrading the reconstruction quality. Here, we present a method for improving the compression efficiency of the FDLP codec by the application of the modified discrete cosine transform (MDCT) for encoding the FDLP residual signals. In the subjective and objective quality evaluations, the proposed FDLP codec provides competent quality of reconstructed signal compared to the state-of-the-art audio codecs for the 32 - 64 kbps range.

关键词： Signal encoding

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Extractive speech summarization by active learning

Extractive speech summarization by active learning

引用

IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Justin Jian Zhang Ricky Ho Yin Chan Pascale Fung Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781424454785

In this paper, we propose an active learning approach for feature-based extractive summarization of lecture speech. Most state-of-the-art speech summarization systems are trained by using a large amount of human reference summaries. Active learning targets to minimize human annotation efforts by automatically selecting a small amount of unlabeled examples for labeling. Our method chooses the unlabeled examples according to a combination of informativeness criterion and robustness criterion. Our summarization results show an increasing learning curve of ROUGE-L F-measure, from 0.44 to 0.54, consistently higher than that of using randomly chosen training samples. We also show that, by following the rhetorical structure in presentation slides, it is possible for humans to produce "gold standard" reference summaries with very high inter-labeler agreement.

关键词： humans Labeling Data mining Stability Reproducibility of results Natural languages Guidelines Speech analysis Supervised learning Training data

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Applications of signal analysis using autoregressive models for amplitude modulation

Applications of signal analysis using autoregressive models ...

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2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009

作者： Ganapathy, Sriram Thomas, Samuel Motlicek, Petr Hermansky, Hynek Department of Electrical and Computer Engineering Johns Hopkins University United States Idiap Research Institute Martigny Switzerland Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781424436798

Frequency Domain Linear Prediction (FDLP) represents an efficient technique for representing the long-term amplitude modulations (AM) of speech/audio signals using autoregressive models. For the proposed analysis technique, relatively long temporal segments (1000 ms) of the input signal are decomposed into a set of sub-bands. FDLP is applied on each sub-band to model the temporal envelopes. The residual of the linear prediction represents the frequency modulations (FM) in the sub-band signal. In this paper, we present several applications of the proposed AM-FM decomposition technique for a variety of tasks like wide-band audio coding, speech recognition in reverberant environments and robust feature extraction for phoneme recognition. ©2009 IEEE.

关键词： Amplitude modulation

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Using citations to generate surveys of scientific paradigms

Using citations to generate surveys of scientific paradigms

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human language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009

作者： Mohammad, Saif Dorr, Bonnie Egan, Melissa Hassan, Ahmed Muthukrishan, Pradeep Qazvinian, Vahed Radev, Dragomir Zajic, David Institute for Advanced Computer Studies University of Maryland United States Computer Science University of Maryland United States Human Language Technology Center of Excellence United States Center for Advanced Study of Language United States Department of Electrical Engineering and Computer Science University of Michigan United States School of Information University of Michigan United States

ISBN: (纸本)9781932432411

The number of research publications in various disciplines is growing exponentially. Researchers and scientists are increasingly finding themselves in the position of having to quickly understand large amounts of technical material. In this paper we present the first steps in producing an automatically generated, readily consumable, technical survey. Specifically we explore the combination of citation information and summarization techniques. Even though prior work (Teufel et al., 2006) argues that citation text is unsuitable for summarization, we show that in the framework of multi-document survey creation, citation texts can play a crucial role. © 2009 Association for Computational Linguistics.

关键词： Surveys

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Toward machine translation with statistics and syntax and semantics

Toward machine translation with statistics and syntax and se...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Dekai Wu Department of Computer Science & Engineering Human Language Technology Center Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781424454785

In this paper, we survey some central issues in the historical, current, and future landscape of statistical machine translation (SMT) research, taking as a starting point an extended three-dimensional MT model space. We posit a socio-geographical conceptual disparity hypothesis, that aims to explain why language pairs like Chinese-English have presented MT with so much more difficulty than others. The evolution from simple token-based to segment-based to tree-based syntactic SMT is sketched. For tree-based SMT, we consider language bias rationales for selecting the degree of compositional power within the hierarchy of expressiveness for transduction grammars (or synchronous grammars). This leads us to inversion transductions and the ITG model prevalent in current state-of-the-art SMT, along with the underlying ITG hypothesis, which posits a language universal. Against this backdrop, we enumerate a set of key open questions for syntactic SMT. We then consider the more recent area of semantic SMT. We list principles for successful application of sense disambiguation models to semantic SMT, and describe early directions in the use of semantic role labeling for semantic SMT.

关键词： Statistics Surface-mount technology Space technology Machine learning humans computer science Labeling Hardware Speech recognition Pattern recognition

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