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Speaker adaptive bottleneck features extraction for LVCSR based on discriminative learning of speaker codes

Speaker adaptive bottleneck features extraction for LVCSR ba...

引用

International Symposium on Chinese Spoken language processing

作者： Changqing Kong Shaofei Xue Jianqing Gao Wu Guo Lirong Dai Hui Jiang National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Iflytek Research Anhui USTC iFlytek Co. Ltd. Department of Electrical Engineering and Computer Science York University

Recently, several fast speaker adaptation methods based on the so-called speaker codes (SC) have been proposed for the hybrid DNN-HMM speech recognition model [1, 2, 3]. In these methods the target speaker features are modified to match the given speaker-independent models or the speaker-independent models are transformed towards one particular speaker based on the discriminative learning of speaker codes. Previous researches have shown that these proposed SC-based adaptation methods are very effective to adapt large DNN models using only a small amount of adaptation data. In this work, we have explored the combination of direct speaker adaptation technique in model space based on speaker codes (mSA-SC) and bottleneck features where mSA-SC is used as an extraction instrument of speaker adaptive bottleneck features. We have evaluated the proposed speaker adaptive bottleneck features extraction method in two speech recognition tasks, namely PSC Mandarin task and large scale 320-hr Switchboard task. Experimental results have verified that it is quite suitable for very large scale tasks. For example, the Switchboard results have shown that it can achieve relative 9% reduction in word error rate on an unsupervised speaker adaptation scheme.

关键词： Hidden Markov models Neural networks Adaptation models Feature extraction speech recognition Switches Training

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Patient level analytics using self-organising maps: A case study on Type-1 Diabetes self-care survey responses

Patient level analytics using self-organising maps: A case s...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Santosh Tirunagari Norman Poh Kouros Aliabadi David Windridge Deborah Cooke University of Surrey Guildford Surrey United Kingdom Center for Vision Speech and Signal Processing Department of Computing Department of Computer Science Middlesex University The Burroughs London School of Health Sciences

Survey questionnaires are often heterogeneous because they contain both quantitative (numeric) and qualitative (text) responses, as well as missing values. While traditional, model-based methods are commonly used by clinicians, we deploy Self Organizing Maps (SOM) as a means to visualise the data. In a survey study aiming at understanding the self-care behaviour of 611 patients with Type-1 Diabetes, we show that SOM can be used to (1) identify co-morbidities; (2) to link self-care factors that are dependent on each other; and (3) to visualise individual patient profiles; In evaluation with clinicians and experts in Type-1 Diabetes, the knowledge and insights extracted using SOM correspond well to clinical expectation. Furthermore, the output of SOM in the form of a U-matrix is found to offer an interesting alternative means of visualising patient profiles instead of a usual tabular form.

关键词： Data visualization Diabetes Neurons Correlation Retinopathy Vectors Proteins

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Combining Bilingual and Comparable Corpora for Low Resource Machine Translation 8

Combining Bilingual and Comparable Corpora for Low Resource ...

引用

8th Workshop on Statistical Machine Translation, WMT 2013

作者： Irvine, Ann Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University Computer and Information Science Dept. University of Pennsylvania United States

ISBN: (纸本)9781937284572

Statistical machine translation (SMT) performance suffers when models are trained on only small amounts of parallel data. The learned models typically have both low accuracy (incorrect translations and feature scores) and low coverage (high out-of-vocabulary rates). In this work, we use an additional data resource, comparable corpora, to improve both. Beginning with a small bitext and corresponding phrase-based SMT model, we improve coverage by using bilingual lexicon induction techniques to learn new translations from comparable corpora. Then, we supplement the model's feature space with translation scores estimated over comparable corpora in order to improve accuracy. We observe improvements between 0.5 and 1.7 BLEU translating Tamil, Telugu, Bengali, Malayalam, Hindi, and Urdu into English. © 2013 Association for Computational Linguistics

关键词： Machine translation

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Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals 2

Supervised Bilingual Lexicon Induction with Multiple Monolin...

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2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL-HLT 2013

作者： Irvine, Ann Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University United States Computer and Information Science Dept. University of Pennsylvania United States

ISBN: (纸本)9781937284473

Prior research into learning translations from source and target language monolingual texts has treated the task as an unsupervised learning problem. Although many techniques take advantage of a seed bilingual lexicon, this work is the first to use that data for supervised learning to combine a diverse set of signals derived from a pair of monolingual corpora into a single discriminative model. Even in a low resource machine translation setting, where induced translations have the potential to improve performance substantially, it is reasonable to assume access to some amount of data to perform this kind of optimization. Our work shows that only a few hundred translation pairs are needed to achieve strong performance on the bilingual lexicon induction task, and our approach yields an average relative gain in accuracy of nearly 50% over an unsupervised baseline. Large gains in accuracy hold for all 22 languages (low and high resource) that we investigate. © 2013 Association for Computational Linguistics

关键词： Translation (languages)

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Supervised bilingual lexicon induction with multiple monolingual signals

Supervised bilingual lexicon induction with multiple monolin...

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2013

ISBN: (纸本)9781937284473

关键词： Translation (languages)

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PPDB: The Paraphrase Database 2

PPDB: The Paraphrase Database

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作者： Ganitkevitch, Juri van Durme, Benjamin Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Computer and Information Science Department University of Pennsylvania United States

ISBN: (纸本)9781937284473

We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 million lexical paraphrases, as well as 140 million paraphrase patterns, which capture many meaning-preserving syntactic transformations. The paraphrases are extracted from bilingual parallel corpora totaling over 100 million sentence pairs and over 2 billion English words. We also release PPDB:Spa, a collection of 196 million Spanish paraphrases. Each paraphrase pair in PPDB contains a set of associated scores, including paraphrase probabilities derived from the bitext data and a variety of monolingual distributional similarity scores computed from the Google n-grams and the Annotated Gigaword corpus. Our release includes pruning tools that allow users to determine their own precision/recall tradeoff. © 2013 Association for Computational Linguistics

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PPDB: The paraphrase database

PPDB: The paraphrase database

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2013

作者： Ganitkevitch, Juri Van Durme, Benjamin Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Computer and Information Science Department University of Pennsylvania United States

ISBN: (纸本)9781937284473

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Joshua 5.0: Sparser, better, faster, server 8

Joshua 5.0: Sparser, better, faster, server

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8th Workshop on Statistical Machine Translation, WMT 2013

作者： Post, Matt Ganitkevitch, Juri Orland, Luke Weese, Jonathan Cao, Yuan Callison-Burch, Chris Human Language Technology Center of Excellence Johns Hopkins University Center for Language and Speech Processing Johns Hopkins University Computer and Information Sciences Department University of Pennsylvania United States

ISBN: (纸本)9781937284572

We describe improvements made over the past year to Joshua, an open-source translation system for parsing-based machine translation. The main contributions this past year are significant improvements in both speed and usability of the grammar extraction and decoding steps. We have also rewritten the decoder to use a sparse feature representation, enabling training of large numbers of features with discriminative training methods. © 2013 Association for Computational Linguistics

关键词： Open systems

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Sequential UBM adaptation for speaker verification

Sequential UBM adaptation for speaker verification

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2013 IEEE China Summit and International Conference on Signal and Information processing, ChinaSIP 2013

作者： Wang, Jun Wang, Dong Wu, Xiaojun Zheng, Thomas Fang Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China Center for Speech and Language Technologies Research Institute of Information Technology Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781479910434

GMM-UBM-based speaker verification heavily relies on a well trained UBM. In practice, it is not often easy to obtain an UBM that fully matches acoustic channels in operation. To solve this problem, we propose a novel sequential MAP adaptation approach: by being sequentially updated with data from new enrollments, the UBM learns and converges to the working channel. Our experiments are conducted on a time-varying speech database, with two channel-mismatched UBMs as the initial model. The results confirm that the sequential UBM adaptation provides significant performance improvement, leading to a relative EER reduction of 6.3% and 14.8% for the two mismatched UBMs, respectively. © 2013 IEEE.

关键词： speech recognition

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TASK-DRIVEN ATTENTIONAL MECHANISMS FOR AUDITORY SCENE RECOGNITION

TASK-DRIVEN ATTENTIONAL MECHANISMS FOR AUDITORY SCENE RECOGN...

引用

IEEE International Conference on Acoustics, speech, and Signal processing

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

ISBN: (纸本)9781479903573

How do humans attend to and pick out relevant auditory objects amongst all other sounds in the environment? Based on neurophysiological findings we propose two task oriented attentional mechanisms acting as Bayesian priors which act on two separate levels of processing: a sensory mapping stage and object representation stage. The former sensory stage is modeled as a high dimensional mapping which captures the spectrotemporal nuances and cues of auditory objects. The latter object representation stage then captures the statistical distribution of the different classes of acoustic scenes. This scheme shows a relative improvement in performance by 81 % compared to a baseline system.

关键词： Auditory Attention Acoustic Scene Analysis Sensory processing Object based attention Sensory Process statistical distribution capture

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