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Unsupervised vs. supervised weight estimation for semantic MT evaluation metrics 6

Unsupervised vs. supervised weight estimation for semantic M...

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6th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACT 2012

作者： Lo, Chi-Kiu Wu, Dekai HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781937284381

We present an unsupervised approach to estimate the appropriate degree of contribution of each semantic role type for semantic translation evaluation, yielding a semantic MT evaluation metric whose correlation with human adequacy judgments is comparable to that of recent supervised approaches but without the high cost of a human-ranked training corpus. Our new unsupervised estimation approach is motivated by an analysis showing that the weights learned from supervised training are distributed in a similar fashion to the relative frequencies of the semantic roles. Empirical results show that even without a training corpus of human adequacy rankings against which to optimize correlation, using instead our relative frequency weighting scheme to approximate the importance of each semantic role type leads to a semantic MT evaluation metric that correlates comparable with human adequacy judgments to previous metrics that require far more expensive human rankings of adequacy over a training corpus. As a result, the cost of semantic MT evaluation is greatly reduced. © 2012 Association for Computational Linguistics.

关键词： Semantics

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Adaptation transforms of auto-associative neural networks as features for speaker verification

Adaptation transforms of auto-associative neural networks as...

引用

Speaker and language Recognition Workshop, Odyssey 2012

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

ISBN: (纸本)9789810730932

We present a new approach of using Auto-Associative Neural Networks (AANNs) in the conventional GMM speaker verification framework with i-vector feature extraction and PLDA modeling. In this technique, an i-vector feature extractor is trained using adaptation parameters from a mixture of AANNs. In order to model parts of each speaker's acoustic space, a training objective function based on posterior probabilities of broad phonetic classes is used. The AANN based i-vectors are fused with GMM based i-vectors and a joint PLDA model is trained. The proposed approach provides promising results and significant gains when combined with baseline systems on the telephone conditions of NIST SRE 2010 and the recently concluded IARPA BEST 2011 speaker evaluations. © Odyssey 2012 - Speaker and language Recognition Workshop. All rights reserved.

关键词： Speech recognition

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RESOURCE DEVELOPMENT AND EXPERIMENTS IN AUTOMATIC SOUTH AFRICAN BROADCAST NEWS TRANSCRIPTION 3

RESOURCE DEVELOPMENT AND EXPERIMENTS IN AUTOMATIC SOUTH AFRI...

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3rd Workshop on Spoken language Technologies for Under-Resourced languages, SLTU 2012

作者： Kamper, Herman de Wet, Febe Hain, Thomas Niesler, Thomas Department of Electrical and Electronic Engineering Stellenbosch University South Africa Human Language Technology Competency Area CSIR Meraka Institute Pretoria South Africa Department of Computer Science University of Sheffield United Kingdom

We present a description of the development and evaluation of a first South African broadcast news transcription system. We describe a number of speech resources which have been collected in the resource-scarce South African environment for system development purposes: a 20 hour corpus of South African English (SAE) broadcast news;a 109M word corpus of South African newspaper text collected for language modelling purposes;and a 60k word SAE pronunciation dictionary. The development of our system is based on similar state-of-the-art broadcast news transcription systems and uses cross-word triphone HMMs, MF-PLP features and per-segment cepstral mean and per-bulletin cepstral variance normalisation. Our final system achieves a word error rate of 24.6%. We find that reasonable performance is achieved on newsreader speech while poor performance is achieved on spontaneous and telephone speech in our test data. Finally, we consider the recognition of MP3-compressed audio and show that performance deteriorates only at low bit-rates. © SLTU 2012. All Rights Reserved.

关键词： Modeling languages

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Lowresource speech recognition with automatically learned sparse inverse covariance matrices

Lowresource speech recognition with automatically learned sp...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weibin Zhang Pascale Fung HKUST Human Language Technology Center Department of Electronic and Computer Engineering University of Science and Technology (HKUST) Hong Kong China

Full covariance acoustic models trained with limited training data generalize poorly to unseen test data due to a large number of free parameters. We propose to use sparse inverse covariance matrices to address this problem. Previous sparse inverse covariance methods never outperformed full covariance methods. We propose a method to automatically drive the structure of inverse covariance matrices to sparse during training. We use a new objective function by adding L1 regularization to the traditional objective function for maximum likelihood estimation. The graphic lasso method for the estimation of a sparse inverse covariance matrix is incorporated into the Expectation Maximization algorithm to learn parameters of HMM using the new objective function. Experimental results show that we only need about 25% of the parameters of the inverse covariance matrices to be nonzero in order to achieve the same performance of a full covariance system. Our proposed system using sparse inverse covariance Gaussians also significantly outperforms a system using full covariance Gaussians trained on limited data.

关键词： Covariance matrix Hidden Markov models Training data Linear programming Sparse matrices Training Maximum likelihood estimation

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Phrase-level transduction model with reordering for spoken to written language transformation

Phrase-level transduction model with reordering for spoken t...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ping Xu Pascale Fung Ricky Chan Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

This paper proposes a first-ever phrase-level transduction model with reordering to transform colloquial speech directly to written-style transcription. This model is capable of performing n-m transductions. Our transduction model is trained from a parallel corpus of verbatim transcription and written-style transcription. Deletions, substitutions, insertions are well represented using this model. Inversion transduction cases can also be identified and represented. We implement our transduction model using weighted finite-state transducers (WFSTs), and integrate it into a WFST-based speech recognition search space to give both verbatim speaking-style and written-style transcriptions. Evaluations of our model on Cantonese speech to standard written Chinese show 11.59% relative Word Error Rate (WER) reduction over interpolated language model between Cantonese and standard Chinese speech, 5.72% relative WER reduction and 14.82% relative Bilingual Evaluation Understudy (BLEU) improvement over the word-level transduction model.

关键词： Speech Hidden Markov models Speech recognition Transducers Standards Decoding Computational modeling

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Towards a predicate-argument evaluation for MT 6

Towards a predicate-argument evaluation for MT

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6th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACT 2012

作者： Bojar, Ondřej Wu, Dekai Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781937284381

HMEANT (Lo and Wu, 2011a) is a manual MT evaluation technique that focuses on predicate-argument structure of the sentence. We relate HMEANT to an established linguistic theory, highlighting the possibilities of reusing existing knowledge and resources for interpreting and automating HMEANT. We apply HMEANT to a new language, Czech in particular, by evaluating a set of English-to-Czech MT systems. HMEANT proves to correlate with manual rankings at the sentence level better than a range of automatic metrics. However, the main contribution of this paper is the identification of several issues of HMEANT annotation and our proposal on how to resolve them. © 2012 Association for Computational Linguistics.

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Cross-Lingual language Modeling with Syntactic Reordering for Low-Resource Speech Recognition 12

Cross-Lingual Language Modeling with Syntactic Reordering fo...

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Conference on Empirical Methods in Natural language Processing

作者： Ping Xu Pascale Fung Human Language Technology Center Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781622765034

This paper proposes cross-lingual language modeling for transcribing source resource-poor languages and translating them into target resource-rich languages if necessary. Our focus is to improve the speech recognition performance of low-resource languages by leveraging the language model statistics from resource-rich languages. The most challenging work of cross-lingual language modeling is to solve the syntactic discrepancies between the source and target languages. We therefore propose syntactic reordering for cross-lingual language modeling, and present a first result that compares inversion transduction grammar (ITG) reordering constraints to IBM and local constraints in an integrated speech transcription and translation system. Evaluations on resource-poor Cantonese speech transcription and Cantonese to resource-rich Mandarin translation tasks show that our proposed approach improves the system performance significantly, up to 3.4% relative WER reduction in Cantonese transcription and 13.3% relative bilingual evaluation understudy (BLEU) score improvement in Mandarin transcription compared with the system without reordering.

关键词： modelling languages Speech recognition Cantonese syntax language System performance Transcription Genetic Transcription

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Unsupervised vs. supervised weight estimation for semantic MT evaluation metrics 12

Unsupervised vs. supervised weight estimation for semantic M...

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Workshop on Syntax and Structure in Statistical Translation

作者： Chi-kiu Lo Dekai Wu HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781627483452

关键词： Semantics weight estimation ADEQUACY machine translation MT Metronidazole Comparable

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Towards a Predicate-Argument Evaluation for MT 12

Towards a Predicate-Argument Evaluation for MT

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Workshop on Syntax and Structure in Statistical Translation

作者： Ondrej Bojar Dekai Wu Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781627483452

HMEANT (Lo and Wu, 2011a) is a manual MT evaluation technique that focuses on predicate-argument structure of the sentence. We relate HMEANT to an established linguistic theory, highlighting the possibilities of reusing existing knowledge and resources for interpreting and automating HMEANT. We apply HMEANT to a new language, Czech in particular, by evaluating a set of English-to-Czech MT systems. HMEANT proves to correlate with manual rankings at the sentence . level better than a range of automatic metrics. However, the main contribution of this paper is the identification of several issues of HMEANT annotation and our proposal on how to resolve them.

关键词： machine translation MT Linguistic Theory Probability manual evaluation method Adjudication Automation

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New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure

New ℌ∞ bounds for the recursive least squares algorithm ex...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Koby Crammer Alex Kulesza Mark Dredze Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

The recursive least squares (RLS) algorithm is well known and has been widely used for many years. Most analyses of RLS have assumed statistical properties of the data or the noise process, but recent robust ℌ ∞ analyses have been used to bound the ratio of the performance of the algorithm to the total noise. In this paper, we provide an additive analysis bounding the difference between performance and noise. Our analysis provides additional convergence guarantees in general, and particular benefits for structured input data. We illustrate the analysis using human speech and white noise.

关键词： Noise Additives Signal processing algorithms Algorithm design and analysis Speech Learning systems Prediction algorithms

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