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9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

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Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture

Markov Chain Monte Carlo inference on graphical models using...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Daniel R. Mendat Sang Chin Steve Furber Andreas G. Andreou Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland Applied Physics Laboratory Johns Hopkins University Laurel Maryland School of Computer Science The University of Manchester UK Center for Language and Speech Processing Johns Hopkins University Baltimore Maryland

We present a combined hardware/software architecture to perform Markov Chain Monte Carlo sampling on probabilistic graphical models in a brain-inspired, energy-aware manner. By combining massively-parallel neuromorphic hardware architecture (SpiNNaker) with algorithms we've have developed for the event-based framework employed in SpiNNaker, we achieve large speedups when performing inference as compared to a traditional PC. We present results from two sampling approaches both well suited to the SpiNNaker architecture. Neural sampling, the first of the two approaches relies directly on simulating networks of spiking neurons while the second, Gibb's sampling is more flexible but still takes advantage of the hardware's event-handling capabilities.

关键词： Neurons Graphical models Markov processes Bayes methods Roads computer architecture MATLAB

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Low-resource open vocabulary keyword search using point process models 15

Low-resource open vocabulary keyword search using point proc...

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15th Annual Conference of the International speech Communication Association: Celebrating the Diversity of Spoken languages, INTERspeech 2014

作者： Liu, Chunxi Jansen, Aren Chen, Guoguo Kintzley, Keith Trmal, Jan Khudanpur, Sanjeev Center for Language and Speech Processing Department of Electrical and Computer Engineering United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States

The point process model (PPM) for keyword search is a whole-word parametric modeling framework based on the timing of phonetic events rather than the evolution of frame-level phonetic likelihoods. Recent progress in PPM training and decoding algorithms has yielded state-of-the-art phonetic search performance in high-resource settings, both in terms of accuracy and computational efficiency. In this paper, we consider PPM application to low-resource settings where the amount of transcribed speech is severely limited and the pronunciation dictionary is incomplete. By using (i) state-of-the-art deep neural network acoustic models to generate phonetic events and (ii) grapheme-to-phoneme conversion to generate pronunciations for out-of-vocabulary (OOV) keywords, we find the PPM system reaches state-of-the-art OOV search performance at a small computational cost. Moreover, due to their complementary methodologies, combining PPM outputs with the LVCSR baseline produces average relative ATWV improvements of 7% and 50% for in-vocabulary and OOV keywords, respectively (16% overall). Copyright © 2014 ISCA.

关键词： Deep neural networks

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Modeling predictive perceptual representation of Thai initial consonants

Modeling predictive perceptual representation of Thai initia...

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Oriental COCOSDA International Conference on speech Database and Assessments

作者： P. Phienphanich C. Onsuwan C. Tantibundhit N. Saimai T. Saimai Department of Electrical and Computer Engineering Faculty of Engineering Department of Linguistics Faculty of Liberal Arts Center of Excellence in Intelligent Informatics Speech and Language Technology and Service Innovation (CILS) Thammasat University Pathumthani Thailand

This work is an extension of our previous attempt to construct a spatial representation of 21 initial consonants in Thai by partitioning them into homogeneous clusters based on empirical measures of confusability and distance among phonemes. The measures were taken from perceptual identification performance of 28 listeners (seven full subjects) when stimuli were presented in noise. In present study, two methods of clustering, namely Multidimensional scaling analysis and k-means clustering were employed, yielding six different classifications and four perceptually relevant categories: intra-cluster short distance, intra-cluster long distance, inter-cluster short distance, and inter-cluster long distance. Another set of perceptual experiment (eight listeners; two full subjects) was carried out to verify the predictions. The findings reveal that the derived perceptual clusters and defined categories fit relatively well with the listeners' performance. Distinctive feature systems in phonological theory appear to provide some basis for the clustering of phonemes.

关键词： System-on-chip Noise measurement Noise Psychology speech

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TASK-DRIVEN ATTENTIONAL MECHANISMS FOR AUDITORY SCENE RECOGNITION

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

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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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Phonetically balanced and psychometrically equivalent monosyllabic word lists for word recognition testing in Thai

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Proceedings of Meetings on Acoustics 2015年第1期25卷

作者： Sajeerat Poonyaban Pasinee Aungsakulchai Charturong Tantibundhit Chutamanee Onsuwan Rattinan Tiravanitchakul Krit Kosawat Adirek Munthuli 1Center of Excellence in Intelligent Informatics Speech and Language Technology and Service Innovation (CILS) Thammasat University Thailand 2Department of Electrical and Computer Engineering Faculty of Engineering Thammasat University Rangsit Campus Khlong Luang Pathum Thani Thailand pond_poonyaban@*** meilyp.aung@*** tchartur@engr.tu.ac.th ***@student.tu.ac.th 3Department of English and Linguistics Faculty of Liberral Arts Thammasat University Rangsit Campus Khlong Luang Pathum Thani Thailand consuwan@tu.ac.th 4Department of Communication Science and Disorders Faculty of Medicine Mahidol University Thailand rartv@*** 5NECTEC National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand krit.kosawat@nectec.or.th

Word recognition testing may be defined as a procedure to assess a listener’s ability to identify one-syllable words (such as phonetically-balanced/PB words) that are presented at a given suprathreshold level to arrive at a word recognition score. For Thai, Thammasat University and Ramathibodi Hospital Phonetically Balanced Word Lists 2015 (TU-RAMA PB’15) were created with five lists, each with 25 monosyllabic words. Besides its phoneme distributions being based on large-scale Thai spoken corpora, TU-RAMA PB’15 is in line with TU PB’14 with emphasis on phonetic balance, symmetrical phoneme occurrence, and word familiarity. To evaluate its homogeneity in terms of decibel intelligibility, the lists were recorded and presented to 10 normal hearing participants, ranging from 0 to 50 dB HL in 2 dB increments (ascending order) until they repeated correct verbal responses. Using logistic regression, regression slopes and intercepts were calculated to estimate percentage of correct performance at any given intensity and to construct psychometric functions for every list. Derived psychometric function slopes ranged from 0.2015 to 0.2262 while intensities required for 50% intelligibility ranged from 17.0876 to 20.8856. Two-way Chi-Square analysis performed on both parameters indicated that there was no significant difference among the five lists.

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Predictive analysis of two tone stream segregation via extended Kalman filter

Predictive analysis of two tone stream segregation via exten...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Debmalya Chakrabarty Mounya Elhilali Department of Electrical and Computer Engineering Laboratory of Computational Audio Perception Center for Speech and Language Processing Johns Hopkins University MD USA

Hearing engages in a seemingly effortless way, complex processes that allow our brains to parse the acoustic environment around us into perceptual sound objects, in a phenomenon called streaming or stream segregation. In this paper, we explore the hypothesis that the auditory system relies on the regularity inherent to each stream to segregate it from other competing streams in the scene. Tracking these regularities is achieved via a recursive prediction that tracks the evolution of each stream, using a Kalman filtering approach. The proposed approach combines spectral analysis operating at the level of the auditory periphery with a temporal analysis using Kalman tracking. To incorporate nonlinear relationships in the signal patterns, we employ an extended Kalman filter. This scheme is tested on sinusoidal patterns, or the two tone paradigm. The combined spectral and temporal analysis developed here is able to predict perceptual results of stream segregation by human listeners in a two tone paradigm.

关键词： Indexes Kalman filters Frequency response

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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...

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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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