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In this article, we propose a new method for parametric representation of human’s lips region. The functional diagram of the method is described and implementation details with the explanation of its ...
Building on the work of Peter Hinst and Geo Siegwart, we develop a pragmatised natural deduction calculus, i.e. a natural deduction calculus that incorporates illocutionary operators at the formal lev...
Standard measures of segregation or polarization are inappropriate for high-dimensional data such as Internet browsing histories, item-level purchase data, or text. We develop a model-based measure ...
Two consanguineous families with Uner Tan Syndrome (UTS) were analyzed in relation to self-organizing processes in complex systems, and the evolutionary emergence of human bipedalism. The cases had th...
UBIQUITOUS GIS, DATA ACQUISITION AND SPEECH RECOGNITION.
Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoenco...
People with motor disabilities often face substantial challenges using interfaces designed for manual interaction. Although such obstacles might be partially alleviated by automatic speech recognition...
In this paper, we propose a multi-level, contextdependent acoustic modeling framework for automatic speech recognition. For each context-dependent unit considered by the recognizer, we construct a set...
In this paper, we investigate a closed-loop auditory model and explore its potential as a feature representation for speech recognition. The closed-loop representation consists of an auditory-based, e...
In this paper we propose a back-off discriminative acoustic model for Automatic Speech Recognition (ASR). We use a set of broad phonetic classes to divide the classification problem originating from c...
To advance research, it isimportant to identify prom-ising future research direc-tions, especially those thathave not been adequately pursued or funded in the past. The working group producing this ar...
We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to cl...
SPEECH RECOGNITION WITH LOCALIZED TIME-FR EQUENCY PATTERN DETECTORS.
In this paper, we present an unsupervised method for automatically discovering words from speech using a combination of acoustic pattern discovery, graph clustering, and baseform searching. The algori...
The unsupervised learning of spectro-temporal patterns within speech signals is of interest in a broad range of applications. Where patterns are non-negative and convolutive in nature, relevant learni...

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