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Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal...
The study of the behavior of ion-channels can provide significant information to detect metal ions and small organic molecules in solution. Discrimination of different analytes can be performed by extracting appropriate features from the ion-channel signals and using them for classification. In this paper, we consider features extracted from the Fourier, Wavelet and Walsh-Hadamard domain representations...
Over the last years significant effort has been made to improve the performance of speech recognition. The Fisher Kernel has been suggested as good ways to combine and underlying generative model in the feature space and discriminant classifiers such as SVMs. Chinese name speech patterns are difficult to be classified especially when they are similar in pronunciation. Continuous density hidden Markov...
Distinction of the type of modulated signals is very important in cognitive radio system. In this paper, a novel approach to signal classification is proposed for cognitive radio. Combining the spectral cyclostationary features, embed SVM into the framework of HMM to construct a hybrid HMM/SVM classifier for signal recognition. The simulation results show that the high performance and robustness of...
In this paper we show how common training criteria like for example MPE or MMI can be extended to incorporate a margin term. In addition, a transducer-based training implementation is presented, which covers a large variety of discriminative training criteria for ASR, including the standard MMI, MPE, and MCE criteria, as well as the modifications to these criteria presented here. The modified criteria...
Audio event detection is one of the tasks of the European project VIDIVIDEO. This paper focuses on the detection of non-speech events, and as such only searches for events in audio segments that have been previously classified as non-speech. Preliminary experiments with a small corpus of sound effects have shown the potential of this type of corpus for training purposes. This paper describes our experiments...
Diagnosis of pathological voice is one of the most important issues in biomedical applications of speech technology. This study focuses on the classification of pathological voice using the HMM (hidden Markov model), the GMM (Gaussian mixture model) and a SVM (support vector machine), and then compares the results to work done previously using an ANN (artificial neural network). Speech data were collected...
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