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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011

作者： Zhang, Chao Liu, Yi Zheng, Thomas Fang Tsinghua National Laboratory for Information Science and Technology Division of Technology Innovation and Development Center for Speech and Language Technologies Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

We propose to improve accented speech recognition performance by using asymmetric acoustic model. Our proposed model is generated based on reliable accent specific units and acoustic model reconstruction. The reliable units are extracted with time alignment recognition to cover accent variations at both acoustic and phonetic levels. The asymmetric acoustic model is obtained through selective decision tree merging together with dynamic Gaussian component selection in model reconstruction. The improved resolution of our proposed model is able to handle different levels of accented variations at different degrees. The effectiveness of our approach was evaluated on a typical Chinese accent. Our system outperforms traditional acoustic model reconstruction and MAP adaptation approaches by 8.28% and 7.14%, relatively on Syllable Error Rate (SER) reduction without sacrificing the performance on standard Mandarin speech.

关键词： Speech recognition

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A Dynamical Systems Model for Improving Gestational Weight Gain Behavioral Interventions

A Dynamical Systems Model for Improving Gestational Weight G...

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American Control Conference

作者： Yuwen Dong Daniel E. Rivera Diana M. Thomas Jesus E. Navarro-Barrientos Danielle S. Downs Jennifer S. Savage Linda M. Collins Control Systems Engineering Laboratory (CSEL) School for Engineering of Matter Transport and Energy Arizona State University Tempe AZ USA Department of Mathematical Sciences Montclair State University Montclair NJ USA Society for the Promotion of Applied Computer Science (GFaI) Volmerstr. 3 12489 Berlin Germany Exercise Psychology Laboratory Department of Kinesiology Penn State University University Park PA USA Center for Childhood Obesity Research and the Department of Nutritional Sciences Penn State University University Park PA USA The Methodology Center and Department of Human Development and Family Studies Penn State University University Park PA USA

ISBN: (纸本)9781457710957

Excessive gestational weight gain (GWG) represents a major public health concern. In this paper, we present a dynamical systems model that describes how a behavioral intervention can influence weight gain during pregnancy. The model relies on the integration of a mechanistic energy balance with a dynamical behavioral model. The behavioral model incorporates some well-accepted concepts from psychology: the Theory of Planned Behavior (TPB) and the principle of self-regulation which describes how internal processes within the individual can serve to reinforce the positive outcomes of an intervention. A hypothetical case study is presented to illustrate the basic workings of the model and demonstrate how the proper design of the intervention can counteract natural trends towards declines in healthy eating and reduced physical activity during the course of pregnancy. The model can be used by behavioral scientists to evaluate decision rules for adaptive time-varying behavioral interventions, or as the open-loop model for hybrid model predictive control algorithms acting as decision frameworks for such interventions.

关键词： Adaptation models Educational institutions Mathematical model Predictive models Pregnancy Guidelines USA Councils

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AutoODC: Automated generation of Orthogonal Defect Classifications 11

AutoODC: Automated generation of Orthogonal Defect Classific...

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2011 26th IEEE/ACM International Conference on Automated Software Engineering, ASE 2011

作者： Huang, LiGuo Ng, Vincent Persing, Isaac Geng, Ruili Bai, Xu Tian, Jeff Department of Computer Science and Engineering Southern Methodist University Dallas TX United States Human Language Technology Research Institute University of Texas at Dallas Dallas TX United States

ISBN: (纸本)9781457716393

Orthogonal Defect Classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human intensive and requires experts' knowledge of both ODC and system domains. This paper presents AutoODC, an approach and tool for automating ODC classification by casting it as a supervised text classification problem. Rather than merely apply the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts' ODC experience and domain knowledge into the learning process via proposing a novel Relevance Annotation Framework. We evaluated AutoODC on an industrial defect report from the social network domain. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves an overall accuracy of 80.2% when using manual classifications as a basis of comparison. © 2011 IEEE.

关键词： Classification (of information)

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Estimating document frequencies in a speech corpus

Estimating document frequencies in a speech corpus

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

作者： Karakos, Damianos Dredze, Mark Church, Ken Jansen, Aren Khudanpur, Sanjeev Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD United States Department of Computer Science Johns Hopkins University Baltimore MD United States

ISBN: (纸本)9781467303675

Inverse Document Frequency (IDF) is an important quantity in many applications, including Information Retrieval. IDF is defined in terms of document frequency, df (w), the number of documents that mention w at least once. This quantity is relatively easy to compute over textual documents, but spoken documents are more challenging. This paper considers two baselines: (1) an estimate based on the 1-best ASR output and (2) an estimate based on expected term frequencies computed from the lattice. We improve over these baselines by taking advantage of repetition. Whatever the document is about is likely to be repeated, unlike ASR errors, which tend to be more random (Poisson). In addition, we find it helpful to consider an ensemble of language models. There is an opportunity for the ensemble to reduce noise, assuming that the errors across language models are relatively uncorrelated. The opportunity for improvement is larger when WER is high. This paper considers a pairing task application that could benefit from improved estimates of df. The pairing task inputs conversational sides from the English Fisher corpus and outputs estimates of which sides were from the same conversation. Better estimates of df lead to better performance on this task. © 2011 IEEE.

关键词： Random errors

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Using computer games for instruction: The student experience

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Active Learning in Higher Education 2011年第1期12卷 45-56页

作者： Grimley, Michael Green, Richard Nilsen, Trond Thompson, David Tomes, Russell School of Education Studies and Human Development College of Education University of Canterbury Christchurch Private Bag 4800 New Zealand Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

computer games are fun, exciting and motivational when used as leisure pursuits. But do they have similar attributes when utilized for educational purposes? This article investigates whether learning by computer game can improve student experiences compared with a more formal lecture approach and whether computer games have potential for improving performance. Instruction was split between lectures and computer games, and student experiences were recorded using an Experience Sampling Method to capture real-time experience and feelings of flow. Results indicated that student experiences in the game mode showed increased alertness, increased feelings of being active, increased feelings of involvement and an increased perception of challenge. Flow characteristics revealed boredom during standard lectures but anxiety and flow during game modes. Finally, some evidence of improved attainment was evident. By using contemporary interactive approaches such as computer games, student learning experiences and attainment may be improved. Some practical issues of implementing games are also discussed. © The Author(s) 2011.

关键词： active learning computer games flow instruction learning lecture student experience

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Application of machine learning and numerical analysis to classify tremor in patients affected with essential tremor or Parkinson's disease

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Gerontechnology 2012年第4期10卷 208-219页

作者： Darnall, Nathan D. Donovan, Conrad K. Aktar, Syeda Tseng, Han-Yun Barthelmess, Paulo Cohen, Philip R. Lin, David C. School of Mechanical and Materials Engineering College of Engineering and Architecture Washington State University Pullman WA 99164 United States School of Electrical Engineering and Computer Science College of Engineering and Architecture Washington State University Pullman WA 99164 United States Department of Human Development College of Agricultural Human and Natural Resource Sciences Washington State University Pullman WA 99164 United States Adapx Inc Seattle WA United States Voiland School of Chemical Engineering and Bioengineering Department of Veterinary and Comparative Anatomy Pharmacology and Physiology Pullman WA 99164 United States

The overall goal of this study was to compare the accuracy of various data analysis techniques to quantify tremor severity (TS) in a clinical context, with the aim of improving the reliability (context consistency and inter-rater agreement) of tremor evaluation in patients with Parkinson's disease (PD) or essential tremor (ET). Ten patients with either PD or ET were asked to perform several tasks used in the clinical practice for the characterization of tremor. Three-axis gyroscopes in a Shimmer device measured angular velocities of the wrist of each subject for postural, kinetic, spiral tracing, and resting scenarios, and a digital pen recorded subjects' tracings of an Archimedes spiral printed on paper. Gyroscope data were used for training and testing a supervised machine learning algorithm to classify TS and for root mean squared (RMS) numerical rating of TS, while digital pen data were analyzed numerically to quantify tracing deviations from the spiral and obtain a tremor rating. We evaluated the performance of our proposed methods compared to clinicians' diagnostic rating. The machine learning method matched the clinical rating with 82% accuracy, the digital pen with 78% accuracy, and RMS with 42% accuracy. We obtained the best accuracy of 82% using the decision tree machine learning approach with gyroscope data measured with the Shimmer.

关键词： Digital pen Gyroscope Machine learning Spiral trace Tremor rating

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THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FRENCH, AND GERMAN

THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FREN...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： M. Sundermeyer M. Nussbaum-Thom S. Wiesler C. Plahl A. El-Desoky Mousa S. Hahn D. Nolden R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University

ISBN: (纸本)9781457705380

Recognizing Broadcast Conversational (BC) speech data is a difficult task, which can be regarded as one of the major challenges beyond the recognition of Broadcast News (BN). This paper presents the automatic speech recognition systems developed by RWTH for the English, French, and German language which attained the best word error rates for English and German, and competitive results for the French task in the 2010 Quaero evaluation for BC and BN data. At the same time, the RWTH German system used the least amount of training data among all participants. Large reductions in word error rate were obtained by the incorporation of the new Bottleneck Multilayer Perceptron (MLP) features for all three languages. Additional improvements were obtained for the German system by applying a new language modeling technique, decomposing words into sublexical components.

关键词： automatic speech recognition multilayer perceptrons

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An in-car Chinese noise corpus for speech recognition

An in-car Chinese noise corpus for speech recognition

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2011 International Conference on Asian language Processing, IALP 2011

作者： Hou, Jue Liu, Yi Zhang, Chao Huang, Shilei Center for Speech and Language Technologies Division of Technology Innovation and Development Tsinghua National Laboratory for Information Science and Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Shenzhen Key Laboratory of Intelligent Media and Speech Shenzhen China

ISBN: (纸本)9780769545547

In this paper, we present an in-car Chinese noise corpus that can be used in simulating complicated car environment for robust speech recognition research and experiment. The corpus was collected in mainland China in 2009 and 2010. The corpus includes a diversity of car conditions including different car speed, open/close windows, weather conditions as well as environment conditions. Specially, the rumble strips are also taken into account due to the typical noise generated as the car is passing on. In order to use the corpus efficiently, we performed some acoustic signal analyses on those noise data, mainly focused on stationary properties and energy distribution in the frequency domain. We also performed ASR experiments using selected noise data from the corpus, by adding noise data to clean speech to simulate the in-car environment. The corpus is the first of its kind for in-car Chinese noise corpus, providing abundant and diversified samples for car noise speech recognition task. © 2011 IEEE.

关键词： Speech recognition

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Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition

Hierarchical hybrid MLP/HMM or rather MLP features for a dis...

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IEEE International Conference on Image Processing

作者： Philippe Dreuw Patrick Doetsch Christian Plahl Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

We use neural network based features extracted by a hierarchical multilayer-perceptron (MLP) network either in a hybrid MLP/HMM approach or to discriminatively retrain a Gaussian hidden Markov model (GHMM) system in a tandem approach. MLP networks have been successfully used to model long-term and non-linear features dependencies in automatic speech and optical character recognition. In offline hand writing recognition, MLPs have been mostly used for isolated character and word recognition in hybrid approaches. Here we analyze MLPs within an LVCSR framework for continuous handwriting recognition using discriminative MMI/MPE training. Especially hybrid MLP/HMM and discriminatively retrained MLP-GHMM tandem approaches are evaluated. Significant improvements and competitive results are re ported for a closed-vocabulary task on the IfN/ENIT Arabic handwriting database and for a large-vocabulary task using the IAM English handwriting database.

关键词： Hidden Markov models Handwriting recognition Training Databases Feature extraction Text recognition Character recognition

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Powerful extensions to CRFS for grapheme to phoneme conversion

Powerful extensions to CRFS for grapheme to phoneme conversi...

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

作者： Stefan Hahn Patrick Lehnen Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

Conditional Random Fields (CRFs) have proven to per form well on natural language processing tasks like name transliteration, concept tagging or grapheme-to-phoneme (g2p) conversion. The aim of this paper is to propose some extension to the state-of-the-art CRF systems for these tasks. Since the number of features can grow rapidly, a method for features selection is very helpful to boost performance. A combination of L1 and L2 regularization (elastic net) has been adopted and implemented within the Rprop optimization algorithm. Usually, dependencies on the target side are limited to bigram dependencies since the computational complexity grows exponentially with the history length. We present a modified CRF decoding where a conventional language model on target side is integrated into the CRF search process. Thus, larger contexts can be taken into account. Besides these two main parts, the already published margin-extension to the CRF training criterion has been adopted.

关键词： Training Context Context modeling Optimization Natural language processing Software algorithms Interpolation

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