Automatic continuous speech recognition system employing dynamic programming
First Claim
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1. A speech pattern recognition system for continuous speech composed of a series of words pronounced word by word, said system comprising:
- means for producing from said continuous speech an input pattern A representative of time sequences of feature vectors a1, a2 - - - ai, - - - aI ;
first memory means for storing said input pattern A;
second memory means for storing n reference word patterns Bn, each representing by time sequences of feature vectors b1n, b2n, - - -bjn, - - - bjnn ;
means for reading out a partial pattern A(l,m) which is a part of said input pattern A extending from a time point l to another time point m (1≦
l<
m≦
I), said partial pattern A(l,m) being represented by time sequence of feature vectors al+1, al+2, - - - ai, - - - am ;
first means for calculating through dynamic programming similarity measures S(A(l,m), Bn) between said partial pattern A(l,m) and said reference word pattern Bn ;
means for extracting the maximum value of the partial similarity measures S<
l,m>
with respect to n words;
means for providing a partial recognized result n<
l,m>
which is a word in said n words and by which said partial similarity measure S<
l,m>
is obtained;
third memory means for said partial similarity measure s<
l,m> and
said partial recognized result n<
l,m>
obtained with respect to said time points l and m;
means for dividing said input pattern A to Y partial patterns A(l.sub.(x-1), L.sub.(x)) (X = 1, 2, 3, - - - Y), said input pattern A being composed of Y words and having (Y -
1) breaking points l.sub.(1), l.sub.(2) - - - l.sub.(x) - - - l.sub.(Y-1) ;
means responsive to said partial similarity measure S<
l,m> and
said partial recognized result n<
l,m>
for reading out the partial similarity measures S<
O,l.sub.(1) >
, S<
l.sub.(1),l.sub.(2) >
, - - - S<
l.sub.(x-1), l.sub.(x) >
, - - - S<
lY-1), l.sub.(Y) >
with respect to the combinations (O,l.sub.(1)), (l.sub.(1), l.sub.(2)), - - - (l.sub.(x-1), l.sub.(x)), - - - (l.sub.(Y-1), l.sub.(Y)) of said breaking points;
second means for calculating the maximum value of the sum of said partial similarity measures S>
O,l.sub.(1) >
+S<
l.sub.(1), l.sub.(2) >
+- - - + S<
l.sub.(x-1), l.sub.(x) >
- - - + S<
l.sub.(Y-1), l.sub.(Y) >
; and
means responsive to said second calculating means and said partial recognized result n, m for providing Y words.
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Abstract
A speech pattern recognition system for continuous speech is disclosed. The system includes calculating means which calculates similarity measures between an input pattern and all of the series of patterns including reference word-patterns arranged in all possible orders through a pattern matching process without resorting to a segmentation process. The reference pattern which provides the maximum similarity measure is adopted as the recognized result.
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Citations
5 Claims
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1. A speech pattern recognition system for continuous speech composed of a series of words pronounced word by word, said system comprising:
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means for producing from said continuous speech an input pattern A representative of time sequences of feature vectors a1, a2 - - - ai, - - - aI ; first memory means for storing said input pattern A; second memory means for storing n reference word patterns Bn, each representing by time sequences of feature vectors b1n, b2n, - - -bjn, - - - bjnn ; means for reading out a partial pattern A(l,m) which is a part of said input pattern A extending from a time point l to another time point m (1≦
l<
m≦
I), said partial pattern A(l,m) being represented by time sequence of feature vectors al+1, al+2, - - - ai, - - - am ;first means for calculating through dynamic programming similarity measures S(A(l,m), Bn) between said partial pattern A(l,m) and said reference word pattern Bn ; means for extracting the maximum value of the partial similarity measures S<
l,m>
with respect to n words;means for providing a partial recognized result n<
l,m>
which is a word in said n words and by which said partial similarity measure S<
l,m>
is obtained;third memory means for said partial similarity measure s<
l,m> and
said partial recognized result n<
l,m>
obtained with respect to said time points l and m;means for dividing said input pattern A to Y partial patterns A(l.sub.(x-1), L.sub.(x)) (X = 1, 2, 3, - - - Y), said input pattern A being composed of Y words and having (Y -
1) breaking points l.sub.(1), l.sub.(2) - - - l.sub.(x) - - - l.sub.(Y-1) ;means responsive to said partial similarity measure S<
l,m> and
said partial recognized result n<
l,m>
for reading out the partial similarity measures S<
O,l.sub.(1) >
, S<
l.sub.(1),l.sub.(2) >
, - - - S<
l.sub.(x-1), l.sub.(x) >
, - - - S<
lY-1), l.sub.(Y) >
with respect to the combinations (O,l.sub.(1)), (l.sub.(1), l.sub.(2)), - - - (l.sub.(x-1), l.sub.(x)), - - - (l.sub.(Y-1), l.sub.(Y)) of said breaking points;second means for calculating the maximum value of the sum of said partial similarity measures S>
O,l.sub.(1) >
+S<
l.sub.(1), l.sub.(2) >
+- - - + S<
l.sub.(x-1), l.sub.(x) >
- - - + S<
l.sub.(Y-1), l.sub.(Y) >
; andmeans responsive to said second calculating means and said partial recognized result n, m for providing Y words. - View Dependent Claims (2, 3, 4, 5)
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Specification