Robot dan Manusia
From Word-spotting To Oov Modeling PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

Paul Fitzpatrick (6345g11) 6.345, Automatic Speech Recognition, Spring 2001

Introduction

This paper explores one dimension along which word spotting and speech recognition differ: the nature of the background model. In word spotting, a relatively small number of keywords float on a sea of unknown words. In speech recognition, an occasional unknown word punctuates utterances that are otherwise completely invocabulary. Despite this difference in viewpoint, in some circumstances implementations of the two may become very similar. When transcribed data is available for a domain, word spotting benefits from the more detailed background model this can support [9].

The manner in which the background is modeled in these cases is reminiscent of speech recognition. For example, a large vocabulary with good coverage may be extracted from the corpus, so that relatively few words in an utterance remain unmodeled. In this case, the situation is qualitatively similar to OOV modeling in a conventional speech recognizer, except that the vocabulary is strictly divided into "filler" and "keyword". This paper describes a mechanism for bootstrapping from a relatively weak background model for wordspotting, where OOV words dominate, to a much stronger model where many more word or phrase clusters have been "moved to the foreground" and explicitly modeled. With this increase in vocabulary comes an increase in the potency of language modeling, boosting performance on the original vocabulary.

The following sections show how a conventional speech recognizer can be convinced to cluster frequently occurring acoustic patterns, without requiring the existence of transcribed data.

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Sociable Machines: Expressive Social Exchange Between Humans And Robots PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

Paul Fitzpatrick NE43-936 (Signature of author) 200 Technology Square Cambridge, MA 02139 Date of submission: April, 2002 Expected Date of Completion: April 2003 Laboratory where thesis will be done: MIT Artificial Intelligence Laboratory

Brief Statement of the Problem:

Robotics has proven most successful in narrowly defined domains that ofer suficient constraints to make automated perception and action tractable. The goal of this thesis is to take a step towards generality by developing methods for applying a robot to many diferent narrow domains. This is complementary to the more common research goal of enhancing machine perception and action to deal with wider domains. This approach to extending the range of application of a technology through parameterization rather than generalization is key to fields such as automatic speech recognition. It has the theoretical advantage of providing a framework for factoring context into perception, and the practical advantage of creating systems that do useful work with limited technology.

I propose a scheme for communicating constraints to a mechanically general-purpose robot, so that it can perform novel tasks without needing to first solve open problems in perception and action. In particular, this thesis develops mechanisms for communicating the structure of simple tasks to a robot, translating this structure into a set of supervised learning problems for parts of the task which are dificult to communicate directly, and solving those problems with the guidance of a protocol for inducing feature selection.

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Foundations For A Theory Of Mind For A Humanoid Robot PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

Brian Michael Scassellati Submitted to the Department of Electrical Engineering and Computer Science on May 6, 2001, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science

Abstract

Human social dynamics rely upon the ability to correctly attribute beliefs, goals, and percepts to other people. The set of abilities that allow an individual to infer these hidden mental states based on observed actions and behavior has been called a "theory of mind" (Premack & Woodruff, 1978). Existing models of theory of mind have sought to identify a developmental progression of social skills that serve as the basis for more complex cognitive abilities. These skills include detecting eye contact, identifying self-propelled stimuli, and attributing intent to moving objects. If we are to build machines that interact naturally with people, our machines must both interpret the behavior of others according to these social rules and display the social cues that will allow people to naturally interpret the machine*s behavior.

Drawing from the models of Baron-Cohen (1995) and Leslie (1994), a novel architecture called embodied theory of mind was developed to link high-level cognitive skills to the low-level perceptual abilities of a humanoid robot. The implemented system determines visual saliency based on inherent object attributes, high-level task constraints, and the attentional states of others. Objects of interest are tracked in real-time to produce motion trajectories which are analyzed by a set of naive physical laws designed to discriminate animate from inanimate movement. Animate objects can be the source of attentional states (detected by finding faces and head orientation) as well as intentional states (determined by motion trajectories between objects). Individual components are evaluated by comparisons to human performance on similar tasks, and the complete system is evaluated in the context of a basic social learning mechanism that allows the robot to mimic observed movements.

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Theory Of Mind... For A Robot PDF Print E-mail
Written by Rizki Noor Hidayat Wijayaź   

Brian Scassellati MIT Artificial Intelligence Lab 200 Technology Square Cambridge,MA 02139 http://www.ai.mit.edu/people/scaz/

Abstract

One of the fundamental social skills for humans is a theory of other minds. This set of skills allows us to attribute beliefs, goals, and desires to other individuals. To take part in normal human social dynamics, a robot must not only know about the properties of objects, but also the properties of animate agents in the world. This paper presents the theories of Leslie (1994) and Baron-Cohen (1995) on the development of theory of mind in human children and discusses the potential application of both of these theories to building robots with similar capabilities. Initial implementation details and basic skills (such as finding faces and eyes and distinguishing animate from inanimate stimuli) are introduced. We further speculate on the usefulness of a robotic implementation in evaluating and comparing these two models.

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