13,292 research outputs found
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
A new bandwidth selection criterion for using SVDD to analyze hyperspectral data
This paper presents a method for hyperspectral image classification that uses
support vector data description (SVDD) with the Gaussian kernel function. SVDD
has been a popular machine learning technique for single-class classification,
but selecting the proper Gaussian kernel bandwidth to achieve the best
classification performance is always a challenging problem. This paper proposes
a new automatic, unsupervised Gaussian kernel bandwidth selection approach
which is used with a multiclass SVDD classification scheme. The performance of
the multiclass SVDD classification scheme is evaluated on three frequently used
hyperspectral data sets, and preliminary results show that the proposed method
can achieve better performance than published results on these data sets
- …