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共 164 条记录，以下是111-120 订阅

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Factor analysis of mixture of auto-associative neural networks for speaker verification

Factor analysis of mixture of auto-associative neural networ...

引用

Speaker and language Recognition Workshop, Odyssey 2012

作者： Garimella, Sri Hermansky, Hynek Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States

ISBN: (纸本)9789810730932

This paper introduces the theory of factor analysis of the mixture of Auto-Associative Neural Networks (AANNs) with application in speaker verification. First, we formulate the problem of learning a low-dimensional subspace in part of the mixture of AANNs parameter space, and subsequently derive the update equations by minimizing loss function of the mixture. Second, we apply this technique to build a neural network based speaker verification system, in which the low-dimensional subspace is trained to capture both speaker and channel variabilities. This low-dimensional (or i-vector) representation is used as features for the probabilistic linear discriminant analysis (PLDA) model, as in state-of-the-art speaker verification systems. The proposed factor analysis approach shows promising results on the NIST-08 speaker recognition evaluation (SRE), and yields 18% relative improvement in minimum detection cost function (minDCF) over the previously proposed subspace based mixture of AANNs system. © Odyssey 2012 - Speaker and language Recognition Workshop. All rights reserved.

关键词： Mixtures

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

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

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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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Multilingual MLP features for low-resource LVCSR systems

Multilingual MLP features for low-resource LVCSR systems

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Samuel Thomas Sriram Ganapathy Hynek Hermansky Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University USA

We introduce a new approach to training multilayer perceptrons (MLPs) for large vocabulary continuous speech recognition (LVCSR) in new languages which have only few hours of annotated in-domain training data (for example, 1 hour of data). In our approach, large amounts of annotated out-of-domain data from multiple languages are used to train multilingual MLP systems without dealing with the different phoneme sets for these languages. Features extracted from these MLP systems are used to train LVCSR systems in the low-resource language similar to the Tandem approach. In our experiments, the proposed features provide a relative improvement of about 30% in an low-resource LVCSR setting with only one hour of training data.

关键词： Abstracts Nonhomogeneous media Vocabulary speech Hidden Markov models Switches

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Multilevel speech intelligibility for robust speaker recognition

Multilevel speech intelligibility for robust speaker recogni...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Sridhar Krishna Nemala Mounya Elhilali Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD USA

In the real world, natural conversational speech is an amalgam of speech segments, silences and environmental/ background and channel effects. Labeling the different regions of an acoustic signal according to their information levels would greatly benefit all automatic speech processing tasks. In the current work, we propose a novel segmentation approach based on a perception-based measure of speech intelligibility. Unlike segmentation approaches based on various forms of voice-activity detection (VAD), the proposed parsing approach exploits higher-level perceptual information about signal intelligibility levels. This labeling information is integrated into a novel multilevel framework for automatic speaker recognition task. The system processes the input acoustic signal along independent streams reflecting various levels of intelligibility and then fusing the decision scores from the multiple steams according to their intelligibility contribution. Our results show that the proposed system achieves significant improvements over standard baseline and VAD-based approaches, and attains a performance similar to the one obtained with oracle speech segmentation information.

关键词： speech Multilevel systems speech recognition Acoustics Speaker recognition speech processing NIST

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A System for Recognition of On-Line Handwritten Mathematical Expressions

A System for Recognition of On-Line Handwritten Mathematical...

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International Workshop on Frontiers in Handwriting Recognition

作者： Fotini Simistira Vassilis Papavassiliou Vassilis Katsouros George Carayannis Institute for Language and Speech Processing Athena-Research and Innovation Center in Information Communication and Knowledge Technologies Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

We present a system for recognizing online mathematical expressions (ME). Symbol recognition is based on a template elastic matching distance between pen direction features. The structural analysis of the ME is based on extracting the baseline of the ME and then classifying symbols into levels above and below the baseline. The symbols are then sequentially analyzed using six spatial relations and a respective 2d structure is processed to give the resulting MathML representation of the ME. The system was evaluated on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2011 datasets and demonstrates promising results.

关键词： Handwriting recognition Connectors Writing speech recognition Accuracy Hidden Markov models

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A Morphology Based Approach for Binarization of Handwritten Documents

A Morphology Based Approach for Binarization of Handwritten ...

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International Workshop on Frontiers in Handwriting Recognition

作者： Vassilis Papavassiliou Fotini Simistira Vassilis Katsouros George Carayannis Institute for Language and Speech Processing Athena-Research and Innovation Center in Information Communication and Knowledge Technologies Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

Document image binarization is an initial though critical stage towards the recognition of the text components of a document. This paper describes an efficient method based on mathematical morphology for extracting text regions from degraded handwritten document images. The basic stages of our approach are: (a) top-hat-by-reconstruction to produce a filtered image with reasonable even background, (b) region growing starting from a set of seed points and attaching to each seed similar intensity neighboring pixels and (c) conditional extension of the initially detected text regions based on the values of the second derivative of the filtered image. The method was evaluated on the benchmarking dataset of the International Document Image Binarization Contest (DIBCO 2011) and show promising results.

关键词： Image edge detection Estimation Image segmentation Histograms Pattern recognition Image reconstruction Laplace equations

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A review on low-complexity structures and algorithms for the correction of mismatch errors in time-interleaved ADCs

A review on low-complexity structures and algorithms for the...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Christian Vogel Matthias Hotz Shahzad Saleem Katharina Hausmair Michael Soudan FTW Telecommunications Research Center Vienna Vienna Austria Department of Electrical Engineering National University of Computer and Emerging Sciences Islamabad Pakistan Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

In this paper we review the progress in the design of low-complexity digital correction structures and algorithms for time-interleaved ADCs over the last five years. We devise a discrete-time model, state the design problem, and finally derive the algorithms and structures. In particular, we discuss efficient algorithms to design time-varying correction filters as well as iterative structures utilizing polynomial based filters. Finally, we give an outlook to future research questions.

关键词： Circuits and systems Complexity theory Algorithm design and analysis Calibration Time varying systems Polynomials Time frequency analysis

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Multistream robust speaker recognition based on speech intelligibility

Multistream robust speaker recognition based on speech intel...

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Annual Conference on Information Sciences and Systems

作者： Nemala, Sridhar Krishna Elhilali, Mounya Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)9781424498475

Delimiting the most informative voice segments of an acoustic signal is often a crucial initial step for any speech processing system. In the current work, we propose a novel segmentation approach based on a perception-based measure of speech intelligibility. Unlike segmentation approaches based on various forms of voice-activity detection (VAD), the proposed segmentation approach exploits higher-level perceptual information about the signal intelligibility levels. This classification based on intelligibility estimates is integrated into a novel multistream framework for automatic speaker recognition task. The multistream system processes the input acoustic signal along multiple independent streams reflecting various levels of intelligibility and then fusing the decision scores from the multiple steams according to their intelligibility contribution. Our results show that the proposed multistream system achieves significant improvements both in clean and noisy conditions when compared with a baseline and a state-of-the-art voice-activity detection algorithm. © 2011 IEEE.

关键词： speech intelligibility

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Randomized maximum entropy language models

Randomized maximum entropy language models

引用

2011 IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2011

作者： Xu, Puyang Khudanpur, Sanjeev Gunawardana, Asela Department of Electrical and Computer Engineering Center of Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)9781467303675

We address the memory problem of maximum entropy language models(MELM) with very large feature sets. Randomized techniques are employed to remove all large, exact data structures in MELM implementations. To avoid the dictionary structure that maps each feature to its corresponding weight, the feature hashing trick [1] [2] can be used. We also replace the explicit storage of features with a Bloom filter. We show with extensive experiments that false positive errors of Bloom filters and random hash collisions do not degrade model performance. Both perplexity and WER improvements are demonstrated by building MELM that would otherwise be prohibitively large to estimate or store. © 2011 IEEE.

关键词： Data structures

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Efficient subsampling for training complex language models

Efficient subsampling for training complex language models

引用

Conference on Empirical Methods in Natural language processing, EMNLP 2011

作者： Xu, Puyang Gunawardana, Asela Khudanpur, Sanjeev Department of Electrical and Computer Engineering Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)1937284115

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The advantage of the proposed method comes from a more robust and efficient subsampling technique. The original multi-class language modeling problem is transformed into a set of binary problems where each binary classifier predicts whether or not a particular word will occur. We show that the binarized model is as powerful as the standard model and allows us to aggressively subsample negative training examples without sacrificing predictive performance. Empirical results show that we can train MELM and NNLM at 1% ∼ 5% of the standard complexity with no loss in performance. © 2011 Association for Computational Linguistics.

关键词： Modeling languages

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