Site Moderators Only | Page 59 | Institute for Systems Research

Abstract

A denoising mechanism uses chosen signal classes and selected analysis dictionaries. The chosen signal class includes a collection of signals. The analysis dictionaries describe signals. The embedding threshold value is initially determined for a training set of signals in the chosen signal class. The update signal is initialized with a signal corrupted by noise.

Abstract

A probabilistic framework for acoustic-phonetic automatic speech recognition organizes a set of phonetic features into a hierarchy consisting of a broad manner feature sub-hierarchy and a fine phonetic feature sub-hierarchy. Each phonetic feature of said hierarchy corresponds to a set of acoustic correlates and each broad manner feature of said broad manner feature sub-hierarchy is further associated with a corresponding set of acoustic landmarks. A pattern recognizer is trained from a knowledge base of phonetic features and corresponding acoustic correlates.

Denoising Mechanism for Speech Signals using Embedded Thresholds and an Analysis Dictionary

Abstract

Generalized Washout Filters for Control of Nonlinear Systems

Dynamics and Control of Supercavitating Bodies

Language-Based Control of a Sphere/Tilt-Plate Mechanical System

System and method for automatic speech recognition from phonetic features and acoustic landmarks

Abstract

Vision-Based Control of an Inverted Pendulum on a Rotating Platform

Swarms in Three Dimensions

An Object-Oriented Modular Simulation Framework for Semiconductor CVD Processing Systems

Communications Protocol

Simultaneous Diagonalization for Blind Signal Processing

Pages