In many problems of classification, the performances of a classifier are often evaluated by a
factor (rate of error). the factor is not well adapted for the complex real problems, in particular …

Performance of speech recognition systems strongly degrades in the presence of
background noise, like the driving noise inside a car. In contrast to existing works, we aim to …

Drawing on recent progress in auditory neuroscience, we present a novel speech feature
analysis technique based on localized spectro-temporal cepstral analysis of speech. We …

Over the past two decades several hierarchical learning models have been developed and
applied to a diverse range of practical tasks with much success. Little is known, however, as …

This paper proposes methods for combining cepstral and acoustic waveform
representations for a front-end of support vector machine (SVM)-based speech recognition …

In this paper we present a hierarchical large-margin Gaussian mixture modeling framework
and evaluate it on the task of phonetic classification. A two-stage hierarchical classifier is …

Although operator-valued kernels have recently received increasing interest in various
machine learning and functional data analysis problems such as multi-task learning or …

Classification of speech phonemes is challenging, especially under noisy environments,
and hence traditional speech recognition systems do not perform well in the presence of …

In this paper we propose a k-NN/SASH phoneme classification algorithm that competes
favourably with state-ofthe-art methods. We apply a similarity search algorithm (SASH) that …

In this paper we introduce and investigate the performance of a simple framework for
multiclass problems of support vector machine (SVM), we present a new architecture named …