Unsupervised pattern discovery in speech : applications to word acquisition and speaker segmentation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2007.Includes bibliographical references (p. 167-176).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 classify speech into categories defined by a pre-specified inventory of lexical units (i.e. phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating patterns within the speech signal. We show how pattern discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic patterns between spoken utterances. By aggregating information about these matching patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multi-word phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.(cont.) We demonstrate two applications of our pattern discovery procedure. First, we propose and evaluate two methods for automatically identifying sound clusters generated through pattern discovery. Our results show that high identification accuracy can be achieved for single word clusters using a constrained isolated word recognizer. Second, we apply acoustic pattern matching to the problem of speaker segmentation by attempting to find word-level speech patterns that are repeated by the same speaker. When used to segment a ten hour corpus of multi-speaker lectures, we found that our approach is able to generate segmentations that correlate well to independently generated human segmentations.by Alex Seungryong Park.Ph.D

GPU-based implementation of real-time system for spiking neural networks

Real-time simulations of biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and robotics, prosthetics, neurological and neuroscientific modeling. BNNs possess inherently parallel architecture and operate in continuous signal domain. Spiking neural networks (SNNs) are type of BNNs with reduced signal dynamic range: communication between neurons occurs by means of time-stamped events (spikes). SNNs allow reduction of algorithmic complexity and communication data size at a price of little loss in accuracy. Simulation of SNNs using traditional sequential computer architectures results in significant time penalty. This penalty prohibits application of SNNs in real-time systems. Graphical processing units (GPUs) are cost effective devices specifically designed to exploit parallel shared memory-based floating point operations applied not only to computer graphics, but also to scientific computations. This makes them an attractive solution for SNN simulation compared to that of FPGA, ASIC and cluster message passing computing systems. Successful implementations of GPU-based SNN simulations have been already reported. The contribution of this thesis is the development of a scalable GPU-based realtime system that provides initial framework for design and application of SNNs in various domains. The system delivers an interface that establishes communication with neurons in the network as well as visualizes the outcome produced by the network. Accuracy of the simulation is emphasized due to its importance in the systems that exploit spike time dependent plasticity, classical conditioning and learning. As a result, a small network of 3840 Izhikevich neurons implemented as a hybrid system with Parker-Sochacki numerical integration method achieves real time operation on GTX260 device. An application case study of the system modeling receptor layer of retina is reviewed

A new, very massive modular Liquid Argon Imaging Chamber to detect low energy off-axis neutrinos from the CNGS beam. (Project MODULAr)

The paper is considering an opportunity for the CERN/GranSasso (CNGS) neutrino complex, concurrent time-wise with T2K and NOvA, to search for theta_13 oscillations and CP violation. Compared with large water Cherenkov (T2K) and fine grained scintillators (NOvA), the LAr-TPC offers a higher detection efficiency and a lower backgrounds, since virtually all channels may be unambiguously recognized. The present proposal, called MODULAr, describes a 20 kt fiducial volume LAr-TPC, following very closely the technology developed for the ICARUS-T60o, and is focused on the following activities, for which we seek an extended international collaboration: (1) the neutrino beam from the CERN 400 GeV proton beam and an optimised horn focussing, eventually with an increased intensity in the framework of the LHC accelerator improvement program; (2) A new experimental area LNGS-B, of at least 50000 m3 at 10 km off-axis from the main Laboratory, eventually upgradable to larger sizes. A location is under consideration at about 1.2 km equivalent water depth; (3) A new LAr Imaging detector of at least 20 kt fiducial mass. Such an increase in the volume over the current ICARUS T600 needs to be carefully considered. It is concluded that a very large mass is best realised with a set of many identical, independent units, each of 5 kt, "cloning" the technology of the T600. Further phases may foresee extensions of MODULAr to meet future physics goals. The experiment might reasonably be operational in about 4/5 years, provided a new hall is excavated in the vicinity of the Gran Sasso Laboratory and adequate funding and participation are made available.Comment: Correspondig Author: C. Rubbia (E-mail: [email protected]), 33 pages, 11 figure

The sanction for religious education based on the function of religion in social evolution

Thesis (Ph.D.)--Boston Universit

Linear Regression and Unsupervised Learning For Tracking and Embodied Robot Control.

Computer vision problems, such as tracking and robot navigation, tend to be solved using models of the objects of interest to the problem. These models are often either hard-coded, or learned in a supervised manner. In either case, an engineer is required to identify the visual information that is important to the task, which is both time consuming and problematic. Issues with these engineered systems relate to the ungrounded nature of the knowledge imparted by the engineer, where the systems have no meaning attached to the representations. This leads to systems that are brittle and are prone to failure when expected to act in environments not envisaged by the engineer. The work presented in this thesis removes the need for hard-coded or engineered models of either visual information representations or behaviour. This is achieved by developing novel approaches for learning from example, in both input (percept) and output (action) spaces. This approach leads to the development of novel feature tracking algorithms, and methods for robot control. Applying this approach to feature tracking, unsupervised learning is employed, in real time, to build appearance models of the target that represent the input space structure, and this structure is exploited to partition banks of computationally efficient, linear regression based target displacement estimators. This thesis presents the first application of regression based methods to the problem of simultaneously modeling and tracking a target object. The computationally efficient Linear Predictor (LP) tracker is investigated, along with methods for combining and weighting flocks of LP’s. The tracking algorithms developed operate with accuracy comparable to other state of the art online approaches and with a significant gain in computational efficiency. This is achieved as a result of two specific contributions. First, novel online approaches for the unsupervised learning of modes of target appearance that identify aspects of the target are introduced. Second, a general tracking framework is developed within which the identified aspects of the target are adaptively associated to subsets of a bank of LP trackers. This results in the partitioning of LP’s and the online creation of aspect specific LP flocks that facilitate tracking through significant appearance changes. Applying the approach to the percept action domain, unsupervised learning is employed to discover the structure of the action space, and this structure is used in the formation of meaningful perceptual categories, and to facilitate the use of localised input-output (percept-action) mappings. This approach provides a realisation of an embodied and embedded agent that organises its perceptual space and hence its cognitive process based on interactions with its environment. Central to the proposed approach is the technique of clustering an input-output exemplar set, based on output similarity, and using the resultant input exemplar groupings to characterise a perceptual category. All input exemplars that are coupled to a certain class of outputs form a category - the category of a given affordance, action or function. In this sense the formed perceptual categories have meaning and are grounded in the embodiment of the agent. The approach is shown to identify the relative importance of perceptual features and is able to solve percept-action tasks, defined only by demonstration, in previously unseen situations. Within this percept-action learning framework, two alternative approaches are developed. The first approach employs hierarchical output space clustering of point-to-point mappings, to achieve search efficiency and input and output space generalisation as well as a mechanism for identifying the important variance and invariance in the input space. The exemplar hierarchy provides, in a single structure, a mechanism for classifying previously unseen inputs and generating appropriate outputs. The second approach to a percept-action learning framework integrates the regression mappings used in the feature tracking domain, with the action space clustering and imitation learning techniques developed in the percept-action domain. These components are utilised within a novel percept-action data mining methodology, that is able to discover the visual entities that are important to a specific problem, and to map from these entities onto the action space. Applied to the robot control task, this approach allows for real-time generation of continuous action signals, without the use of any supervision or definition of representations or rules of behaviour

An expert system for well-to-well log correlation

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1987.Microfiche copy available in Archives and Science.Bibliography: leaves 65-66.by David J. Lineman.M.S

Special Libraries, July-August 1952

Volume 43, Issue 6https://scholarworks.sjsu.edu/sla_sl_1952/1005/thumbnail.jp

'No-body' is my friend: Cultural considerations of young children's 'imaginary companions'

The variegated relationships young children may have during their early childhood education (ECE) years have long been of interest to theorists and teachers alike. Relationships, and understandings of companionship, can take multiple forms. Our understandings of what these relationships may look like are frequently informed by complex social and cultural constructs; therefore, seeking to apply a one-size-fits-all interpretation is theoretically and culturally problematic, especially when the companions young children may spend time with may be unseen or unnoticed by surrounding adults. A common Euro-western notion is that unseen companions are not real; hence they are generally referred to in the literature as being imaginary. In an extensive literature review, this thesis explores whether companionship with the unseen are one of many possible forms of relationships. Dominant Euro-western claims that such relationships are imaginary are considered critically before the analysis widens in theoretical scope to draw on various alternative cultural accounts. The analysis draws on the theoretical work of Jacques Derrida, Michel Foucault and Karen Barad, in particular. An additional exploration considers the author’s own situated experiences of parenting a child who during her childhood years had companions that were unseen to the author. The final analysis, based on a small selection of carefully chosen autoethnographic accounts, draws on the significance of ancestral inheritance. Within this specific research context that inheritance comprises my daughter’s Māori ancestry and our shared Celtic ancestry. Ancestral inheritance provides reflexive companionship for the entire thesis. Examining inheritance sheds light on ways that traditional Euro-western claims of the imagined worlds of children, and consequently childhood, have produced a legacy of ideas which have subsequently held the term imaginary in place. These dominant worldviews are shown to fall short in their capacity to cover the diversity of lived realities of children and their unseen (to others) companion(s). Inheritance also denotes the culturally mediated birthright that may travel alongside a young child and how this birthright may provide insight and alternative cultural knowledge and understandings of various multiform relationship companions. As a result of analysing several possible perspectives on the relationships under study, this thesis proposes the new term culturally compatible travelling companions (CCTC): companions who travel alongside children as ordinary, expected features of their lifespan. This shift in term acknowledges that diverse notions of companionship, and the forms this may take, are integral and salient constructions of each culture’s historical and narrative accounts. This is timely research, as currently nearly 64% of young children aged between birth and four years of age attend some type of Early Childhood Education (ECE) service in Aotearoa New Zealand (Ministry of Education [MoE], 2019). The national ECE curriculum document Te Whāriki (MoE, 2017) recognises the significance of young children’s relationships and understands those relationships within the rich and complex cultural knowledge patterning that travels alongside a young child. The aim of this research is to add to the theoretical understandings of the forms, functions and features of the multiple, complex relationship companions who may share the lives of young children, whether or not these companions are seen or discernible to those around the children. It suggests an alternative perspective to the dominant view and offers provocations for those who work alongside young children to think differently about whether these relationship companions are indeed only imaginary