Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of th...

Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of th...

Acoustic differences between native accents may prove to be too subtle for straightforward brute force techniques such as blindly clustered Gaussian mixture model (GMM) classifiers to yield satisfactory discrimination performance while these methods work well for classifying more pronounced differences such as language, gender or channel. In this paper it is shown that small channel differences...

Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of th...

Although exemplar based approaches have shown good accuracy in classification problems, some limitations are observed in the accuracy of exemplar based automatic speech recognition (ASR) applications. The main limitation of these algorithms is their high computational complexity which makes them difficult to extend to ASR applications. In this paper, an N-best class selector is introduced based...

Recognizing human emotions/attitudes from speech cues has gained increased attention recently. Most previous work has focused primarily on suprasegmental prosodic features calculated at the utterance level for modeling against details at the segmental phoneme level. Based on the hypothesis that different emotions have varying effects on the properties of the different speech sounds, this paper ...

A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the so-called ”kernel-idea” well-known linear techniques such as Principal Component Analysis(PCA), Linear Discriminant Analysis(LDA) and Independent Component Analysis(ICA) can be non-linearized in a general way. The aim of this paper here is twofold. First, we describe t...

In this paper, we present some practical experiments for continuous speech frame-by-frame phoneme classification using Multi Layer Perceptron (MLP) neural networks. We used to train and test our software application, the the OASIS Numbers speech database. In our experiments, we tried to classify all the existing 32 phonemes together, from OASIS Numbers database dictionary. We also used differen...

Currently, the hidden Markov model (HMM) is the predominant model studied and used for speech recognition. There has been undeniable progress in speech recognition through the study of HMM but the huge gap that exists between user’s expectation and progress is also undeniable. There are essentially two limitation with the HMM: (1) The Markovian structure in HMM leads to limitation in what it ca...