495 research outputs found
Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction
Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
We propose a local modelling approach using deep convolutional neural
networks (CNNs) for fine-grained image classification. Recently, deep CNNs
trained from large datasets have considerably improved the performance of
object recognition. However, to date there has been limited work using these
deep CNNs as local feature extractors. This partly stems from CNNs having
internal representations which are high dimensional, thereby making such
representations difficult to model using stochastic models. To overcome this
issue, we propose to reduce the dimensionality of one of the internal fully
connected layers, in conjunction with layer-restricted retraining to avoid
retraining the entire network. The distribution of low-dimensional features
obtained from the modified layer is then modelled using a Gaussian mixture
model. Comparative experiments show that considerable performance improvements
can be achieved on the challenging Fish and UEC FOOD-100 datasets.Comment: 5 pages, three figure
An investigation of supervector regression for forensic voice comparison on small data
International audienceThe present paper deals with an observer design for a nonlinear lateral vehicle model. The nonlinear model is represented by an exact Takagi-Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral observer (PMIO) based on the TS model is designed to estimate simultaneously the state vector and the unknown input (road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory which guaranties bounded error. Simulations are carried out and experimental results are provided to illustrate the proposed observer
Compensation of Nuisance Factors for Speaker and Language Recognition
The variability of the channel and environment is
one of the most important factors affecting the performance of
text-independent speaker verification systems. The best techniques
for channel compensation are model based. Most of them have
been proposed for Gaussian mixture models, while in the feature
domain blind channel compensation is usually performed. The
aim of this work is to explore techniques that allow more accurate
intersession compensation in the feature domain. Compensating
the features rather than the models has the advantage that the
transformed parameters can be used with models of a different
nature and complexity and for different tasks. In this paper,
we evaluate the effects of the compensation of the intersession
variability obtained by means of the channel factors approach. In
particular, we compare channel variability modeling in the usual
Gaussian mixture model domain, and our proposed feature domain
compensation technique. We show that the two approaches
lead to similar results on the NIST 2005 Speaker Recognition
Evaluation data with a reduced computation cost. We also report
the results of a system, based on the intersession compensation
technique in the feature space that was among the best participants
in the NIST 2006 Speaker Recognition Evaluation. Moreover, we
show how we obtained significant performance improvement in
language recognition by estimating and compensating, in the
feature domain, the distortions due to interspeaker variability
within the same language.
Index Terms—Factor anal
- …