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Investigations on sequence training of neural networks 40

Investigations on sequence training of neural networks

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wiesler, Simon Golik, Pavel Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

In this paper we present an investigation of sequence-discriminative training of deep neural networks for automatic speech recognition. We evaluate different sequence-discriminative training criteria (MMI and MPE) and optimization algorithms (including SGD and Rprop) using the RASR toolkit. Further, we compare the training of the whole network with that of the output layer only. Technical details necessary for a robust training are studied, since there is no consensus yet on the ultimate training recipe. The investigation extends our previous work on training linear bottleneck networks from scratch showing the consistently positive effect of sequence training. © 2015 IEEE.

关键词： deep neural networks optimization sequence training speech recognition

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Sequence-discriminative training of recurrent neural networks 40

Sequence-discriminative training of recurrent neural network...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Voigtlaender, Paul Doetsch, Patrick Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

We investigate sequence-discriminative training of long shortterm memory recurrent neural networks using the maximum mutual information criterion. We show that although recurrent neural networks already make use of the whole observation sequence and are able to incorporate more contextual information than feed forward networks, their performance can be improved with sequence-discriminative training. Experiments are performed on two publicly available handwriting recognition tasks containing English and French handwriting. On the English corpus, we obtain a relative improvement in WER of over 11% with maximum mutual information (MMI) training compared to cross-entropy training. On the French corpus, we observed that it is necessary to interpolate the MMI objective function with cross-entropy. © 2015 IEEE.

关键词： handwriting recognition long shortterm memory recurrent neural networks sequence-discriminative training

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Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables 40

Integrating Gaussian mixtures into deep neural networks: Sof...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tuske, Zoltan Tahir, Muhammad Ali Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781467369978

In the hybrid approach, neural network output directly serves as hidden Markov model (HMM) state posterior probability estimates. In contrast to this, in the tandem approach neural network output is used as input features to improve classic Gaussian mixture model (GMM) based emission probability estimates. This paper shows that GMM can be easily integrated into the deep neural network framework. By exploiting its equivalence with the log-linear mixture model (LMM), GMM can be transformed to a large softmax layer followed by a summation pooling layer. Theoretical and experimental results indicate that the jointly trained and optimally chosen GMM and bottleneck tandem features cannot perform worse than a hybrid model. Thus, the question 'hybrid vs. tandem' simplifies to optimizing the output layer of a neural network. Speech recognition experiments are carried out on a broadcast news and conversations task using up to 12 feed-forward hidden layers with sigmoid and rectified linear unit activation functions. The evaluation of the LMM layer shows recognition gains over the classic softmax output. © 2015 IEEE.

关键词： ASR bottleneck DNN GMM hybrid LMM Log-linear mixture model neural network tandem

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ValenTo: Sentiment Analysis of Figurative language Tweets with Irony and Sarcasm 9

ValenTo: Sentiment Analysis of Figurative Language Tweets wi...

引用

9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Farías, Delia Irazú Hernández Sulis, Emilio Patti, Viviana Ruffo, Giancarlo Bosco, Cristina Universitat Politècnica de València Pattern Recognition and Human Language Technology Spain University of Turin Dipartimento di Informatica Italy

ISBN: (纸本)9781941643402

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only word-sentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets. © 2015 Association for Computational Linguistics

关键词： Sentiment analysis

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Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions 40

Unsupervised adaptation of a denoising autoencoder by Bayesi...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Heymann, Jahn Haeb-Umbach, Reinhold Golik, Pavel Schluter, Ralf University of Paderborn Department of Communications Engineering Paderborn Germany RWTH Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen Aachen Germany

ISBN: (纸本)9781467369978

The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data. © 2015 IEEE.

关键词： deep neuronal networks denoising autoencoder feature enhancement robust speech recognition

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Speeding up 2D-warping for pose-invariant face recognition

Speeding up 2D-warping for pose-invariant face recognition

引用

International Conference on Automatic Face and Gesture recognition

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

Recently, state-of-the-art recognition accuracies for pose-invariant face recognition have been achieved by using 2D-Warping methods in a nearest-neighbor framework. However, the main drawback of these methods is the high computational complexity. In this paper we address this issue. We use a simple and fast method to get a rough estimate of a 2D-Warping. This estimate can then be used to apply an image dependent warprange on the 2D-Warping algorithm, limit the possible poses or preselect the most likely classes. By this method we are able significantly reduce the runtime of a recently proposed 2D-Warping algorithm without sacrificing recognition accuracy.

关键词： Face recognition Accuracy Runtime Databases Complexity theory Manuals Face

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Speaker adaptive joint training of Gaussian mixture models and bottleneck features

Speaker adaptive joint training of Gaussian mixture models a...

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Zoltán Tüske Pavel Golik Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

In the tandem approach, the output of a neural network (NN) serves as input features to a Gaussian mixture model (GMM) aiming to improve the emission probability estimates. As has been shown in our previous work, GMM with pooled covariance matrix can be integrated into a neural network framework as a softmax layer with hidden variables, which allows for joint estimation of both neural network and Gaussian mixture parameters. Here, this approach is extended to include speaker adaptive training (SAT) by introducing a speaker dependent neural network layer. Error backpropagation beyond this speaker dependent layer realizes the adaptive training of the Gaussian parameters as well as the optimization of the bottleneck (BN) tandem features of the underlying acoustic model, simultaneously. In this study, after the initialization by constrained maximum likelihood linear regression (CMLLR) the speaker dependent layer itself is kept constant during the joint training. Experiments show that the deeper backpropagation through the speaker dependent layer is necessary for improved recognition performance. The speaker adaptively and jointly trained BN-GMM results in 5% relative improvement over very strong speaker-independent hybrid baseline on the Quaero English broadcast news and conversations task, and on the 300-hour Switchboard task.

关键词： Adaptation models Training Acoustics Hidden Markov models Robustness Artificial neural networks

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Combining handwriting and speech recognition for transcribing historical handwritten documents

Combining handwriting and speech recognition for transcribin...

引用

International Conference on Document Analysis and recognition

作者： Emilio Granell Carlos-D. Martínez-Hinarejos Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.

关键词： Iterative decoding Acoustics Proposals Laplace equations Integrated optics Optical imaging

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A random forest approach for authorship profiling 16

A random forest approach for authorship profiling

引用

16th Conference and Labs of the Evaluation Forum, CLEF 2015

作者： Palomino-Garibay, Alonso Camacho-González, Adolfo T. Fierro-Villaneda, Ricardo A. Hernández-Farias, Irazú Buscaldi, Davide Meza-Ruiz, Ivan V. Ciudad de Mexico Mexico Ciudad de Mexico Mexico Pattern Recognition and Human Language Technology Universitat Politécnica de Valencia Valencia Spain Universite Paris 13 Sorbonne Paris Cité Villetaneuse France

In this paper we present our approach to extract profile information from anonymized tweets for the author profiling task at PAN 2015 [10]. Particularly we explore the versatility of random forest classifiers for the genre and age groups information and random forest regressions to score important aspects of the personality of a user. Furthermore we propose a set of features tailored for this task based on characteristics of the twitters. In particular, our approach relies on previous proposed features for sentiment analysis tasks.

关键词： Decision trees

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Data Mapping by Restricted Boltzmann Machines for Social Circles Detection

Data Mapping by Restricted Boltzmann Machines for Social Cir...

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International Joint Conference on Neural Networks

作者： Jesús Alonso Roberto Paredes Paolo Rosso Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València

ISBN: (纸本)9781479919611

Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. We compare our results with several different baselines.

关键词： Data Mapping Restricted Boltzmann Machines Social Circles Detection Boltzmann machines Circles Social Media MAPPING(MATHEMATICAL) Interest Research Facebook optimal parameter Social Networks

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