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Title: Time series association learning

Abstract

An acoustic input is recognized from inferred articulatory movements output by a learned relationship between training acoustic waveforms and articulatory movements. The inferred movements are compared with template patterns prepared from training movements when the relationship was learned to regenerate an acoustic recognition. In a preferred embodiment, the acoustic articulatory relationships are learned by a neural network. Subsequent input acoustic patterns then generate the inferred articulatory movements for use with the templates. Articulatory movement data may be supplemented with characteristic acoustic information, e.g. relative power and high frequency data, to improve template recognition.

Inventors:
 [1]
  1. Santa Fe, NM
Issue Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM
OSTI Identifier:
870020
Patent Number(s):
5440661
Assignee:
United States of America as represented by United States (Washington, DC)
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
time; series; association; learning; acoustic; input; recognized; inferred; articulatory; movements; output; learned; relationship; training; waveforms; compared; template; patterns; prepared; regenerate; recognition; preferred; embodiment; relationships; neural; network; subsequent; generate; templates; movement; data; supplemented; characteristic; information; relative; power; frequency; improve; acoustic wave; neural network; preferred embodiment; time series; articulatory movements; neural net; /704/

Citation Formats

Papcun, George J. Time series association learning. United States: N. p., 1995. Web.
Papcun, George J. Time series association learning. United States.
Papcun, George J. Sun . "Time series association learning". United States. https://www.osti.gov/servlets/purl/870020.
@article{osti_870020,
title = {Time series association learning},
author = {Papcun, George J},
abstractNote = {An acoustic input is recognized from inferred articulatory movements output by a learned relationship between training acoustic waveforms and articulatory movements. The inferred movements are compared with template patterns prepared from training movements when the relationship was learned to regenerate an acoustic recognition. In a preferred embodiment, the acoustic articulatory relationships are learned by a neural network. Subsequent input acoustic patterns then generate the inferred articulatory movements for use with the templates. Articulatory movement data may be supplemented with characteristic acoustic information, e.g. relative power and high frequency data, to improve template recognition.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1995},
month = {1}
}

Patent:

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