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Automatic monitoring of the patients with Alzheimer's disease and diagnosis of the disease in early stages can have a significant impact on the society. Here, we investigate an automatic diagnosis approach through the use of features derived from transcriptions of conversations with the subjects. As opposed to standard tests that are mostly focused on memory recall, spontaneous conversations are carried...
The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.
This paper presents work done on classification, segmentation and chronological prediction of cinematic sound employing support vector machines (SVM) with sequential minimal optimization (SMO). Speech, music, environmental sound and silence, plus all pair wise combinations excluding silence, are considered as classes. A model considering simple adjacency rules and probabilistic output from logistic...
A significant amount of the research on automatic emotion recognition from speech focuses on acted speech that is produced by professional actors. This approach often leads to overoptimistic results as the recognition of emotion in real-life conditions is more challenging due the propensity of mixed and less intense emotions in natural speech. The paper presents an empirical study of the most widely...
The HMM/SVM-based two-stage framework has been widely used for automatic phone alignment. The two-stage method uses SVM classifiers to refine the hypothesized boundaries given by the HMM-based Viterbi forced alignment. However, there are two drawbacks in using the classification model for detecting the phone boundaries. First, the training data contains only information about the boundary and far...
This paper investigates how to integrate multi-modal features for story boundary detection in broadcast news. The detection problem is formulated as a classification task, i.e., classifying each candidate into boundary/non-boundary based on a set of features. We use a diverse collection of features from text, audio and video modalities: lexical features capturing the semantic shifts of news topics...
Primary Question detection in online forum is a subtask of extracting question-answer pairs. In this paper, by surveying the forms of questions in Chinese online forums, a combination of textual and N-gram features achieved via feature selection is adopted to help detecting primary questions. By viewing primary question detection a binary classification problem, decision tree classifier C4.5 and support...
Phoneme recognition is an essential component of any robust speech decoder and has been tackled by many researchers. Speech feature extraction constitutes the front end module of any speech decoder: it plays an essential role and has a strong impact on the recognition performance. The research community is aggressively searching for more powerful solutions which combine the existing feature extraction...
Gender identification based on speech signal has become gradually a matter of concern in recent years. In this context 6 feature types including MFCC, LPC, RC, LAR, pitch values and formants are compared for automatic gender identification and three best feature types are selected using four feature selection techniques. These techniques are GMM, decision tree, Fisher's discriminant ratio, and volume...
A new approach of recognizing vowels from articulatory position time-series data was proposed and tested in this paper. This approach directly mapped articulatory position time-series data to vowels without extracting articulatory features such as mouth opening. The input time-series data were time-normalized and sampled to fixed-width vectors of articulatory positions. Three commonly used classifiers,...
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