Mathematical Foundations Modeled after Neo-Cortex for Discovery and Understanding of Structures in Data

2012 ◽  
pp. 224-241
Author(s):  
Shubha Kadambe
Keyword(s):  
Graphical Models ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Learning Algorithm ◽  
Hidden Markov ◽  
Mammalian Brain ◽  
Simple Task ◽  
Beta Process ◽  
Mathematical Foundations

Even though there are distinct areas for different functionalities in the mammalian neo-cortex, it seems to use the same algorithm to understand a large variety of input modalities. In addition, it appears that the neo-cortex effortlessly identifies the correlation among many sensor modalities and fuses information obtained from them. The question then is, can we discover the brain’s learning algorithm and approximate it for problems such as computer vision and automatic speech recognition that the mammalian brain is so good at? The answer is: it is an orders of magnitude problem, i.e., not a simple task. However, we can attempt to develop mathematical foundations based on the understanding of how a human brain learns. This chapter is focused along that direction. In particular, it is focused on the ventral stream – the “what pathway” - and describes common algorithms that can be used for representation and classification of signals from different sensor modalities such as auditory and visual. These common algorithms are based on dictionary learning with a beta process, hierarchical graphical models, and embedded hidden Markov models.

Speech Disorders Recognition using Speech Analysis

2013 ◽  
pp. 494-507 ◽  
Author(s):  
Khaled Necibi ◽  
Halima Bahi ◽  
Toufik Sari
Keyword(s):  
Signal Processing ◽  
Hidden Markov Models ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Speech Disorders ◽  
Speech Analysis ◽  
Physical Problems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Speech disorders are human disabilities widely present in young population but also adults may suffer from such disorders after some physical problems. In this context, the detection and further the correction of such disabilities may be handled by Automatic Speech Recognition (ASR) technology. The first works on the speech disorders detection began early in the 70s and seem to follow the same evolution as those on the ASR. Indeed, these early works were more based on the signal processing techniques. Progressively, systems dealing with speech disorders incorporate more ideas from ASR technology. Particularly, Hidden Markov Models, the state-of-the-art approaches in ASR systems, are used. This chapter reviews systems that use ASR techniques to evaluate pronunciation of people who suffer from speech or voice impairments. The authors investigate the existing systems and present the main innovation and some of the available resources.

Literacy by Way of Automatic Speech Recognition

2011 ◽  
pp. 2074-2118
Author(s):  
Russell Gluck ◽  
John Fulcher
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Warping ◽  
Oral Storytelling ◽  
Pattern Recognition Techniques ◽  
Dynamic Time ◽  
Over Time

The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov models (HMMs), but also the tried-and-proven techniques of dynamic time warping and artificial neural networks (ANNs). The coverage then switches to Gluck’s (2004) draw-talk-write (DTW) process, developed over the past two decades to assist non-text literate people become gradually literate over time through telling and/or drawing their own stories. DTW has proved especially effective with “illiterate” people from strong oral, storytelling traditions. The chapter concludes by relating attempts to date in automating the DTW process using ANN-based pattern recognition techniques on an Apple Macintosh G4™ platform.

scholarly journals CutProtFam-Pred: Detection and classification of putative structural cuticular proteins from sequence alone, based on profile Hidden Markov Models

2014 ◽  
Vol 52 ◽  
pp. 51-59 ◽  
Author(s):  
Zoi S. Ioannidou ◽  
Margarita C. Theodoropoulou ◽  
Nikos C. Papandreou ◽  
Judith H. Willis ◽  
Stavros J. Hamodrakas
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Profile Hidden Markov Models ◽  
Cuticular Proteins

scholarly journals Sequence Classification Using Third-Order Moments

2018 ◽  
Vol 30 (1) ◽  
pp. 216-236
Author(s):  
Rasmus Troelsgaard ◽  
Lars Kai Hansen
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Sequence Data ◽  
Hidden Markov ◽  
Score Function ◽  
Simulated Data ◽  
Discrete Observations ◽  
Third Order ◽  
Leibler Divergence

Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.

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