Pattern recognition method

In a machine implemented voice recognition method, as a first step speech signals are analyzed for feature vectors which are used to compare input signals with prestored reference signals. Patterns of any suitable form are used to calculate a similarity distance measure d.sub.IJ which is tested against a threshold to select likely candidates as a first step. A second step selects the most likely candidate by using "common nature" parameters of phonemes such as relative occurrence. Five embodiments of the second step are disclosed, each using a "common nature" criteria of inference to infer (select) the most likely candidate: PA1 (1) d'.sub.I =W.sub.1,.W.sub.2.W.sub.3 where W is a weighting factor; PA1 (2) d".sub.I =C.sub.I d'.sub.I where C.sub.I is a correction factor; PA1 (3) max p(i,j) where p(i) is the probability of occurrence of the i.sup.th phoneme; PA1 (4) min d'.sub.ij as a variation of max p(i,j); and PA1 (5) N(i) is the numerical similarity of the common characteristics of the selected candidates.

BACKGROUND OF THE INVENTION 
1. Field of the Invention: 
The present invention relates to a pattern recognition method and, 
particularly to an improved pattern recognition method which precisely 
recognizes confusing characters, and phoneme that constitutes voices and 
corresponding to each symbol constituting a language. 
2. Description of the Prior Art: 
According to a conventional pattern recognition method such as a method for 
recognizing letters and voices, an input pattern and a standard pattern 
are subjected to comparison, and a pattern having a category name of the 
standard pattern having an optimum degree of identification is introduced. 
In recognizing the letters, when, for example, a Chinese character " " 
(large) is introduced, the comparison can be generally performed well with 
respect to the following Chinese characters " " (dog) or " " (thick), in 
addition to a standard pattern Chinese character " " (large). In 
recognizing voices, when, for example, the sound /t/ is introduced, the 
comparison can be usually performed well with respect to the same 
voiceless stop consonants such as /p/ or /k/ or with respect to /d/, /z/, 
or /s/ having the same place of articulation. Therefore, there is a great 
probability for developing erroneous recognition among such similar 
patterns, and the ability to perform accurate recognition is decreased. 
In recognizing phonemes, for example, in voice produced by a physical 
phenomenon such as vibration of the vocal organs, the phonemes which 
constitute the voice produced under limited physical conditions such as 
length of the vocal organs, may appear to be greatly affected by the 
preceding or succeeding phoneme and the speed of speech. 
Therefore, it is very difficult to precisely recognize the phoneme. 
In order to overcome the above difficulty, a method was proposed, according 
to which a spoken word containing deformed phonemes was compared as a 
practical recognition unit with a standard pattern. 
According to the above method, however, it was necessary to prepare 
standard patterns of such large units as spoken words consisting of a 
combination of phonemes and, hence, it was necessary to store in the 
memory the standard patterns related to spoken words that were to be 
recognized. Since the memory of a tremendous capacity was necessary, it 
was virtually impossible to construct a voice recognizing apparatus which 
is capable of recognizing any voices like a so-called voice typewriter. 
In order to recognize any voices, therefore, it becomes an essential 
requirement to perform the recognition on the phoneme level. 
As mentioned above, however, the recognition on the phoneme level presents 
the following problems: 
(1) It becomes difficult to perform the recognition as the phoneme is 
deformed. 
(2) A phoneme has a length considerably shorter than that of a word, which 
causes confusion among different phonemes. 
(3) Voice is continuously produced with the passage of time, and it is 
necessary to cut out the phoneme as a sectional pattern from the 
continuous voice pattern. It is, however, very difficult to properly cut 
out the sectional patterns. 
With respect to the above-referenced third problem a system called the 
continuous DP (dynamic programming) matching method has been proposed in 
order to continuously perform the matching of the introduced voice pattern 
with the standard pattern without the need of cutting the continuously 
produced voice pattern after a predetermined period of time, and the 
effectiveness of the continuous DP matching method has been confirmed. See 
Continuous Speech Recognition by Continuous DP Matching" by Ryuichi Oka, 
Technical Report of Acoustic Society of Japan, S78-20. 
To cope with the above-referenced first and second problems, on the other 
hand, methods have been proposed in order to: 
(i) Increase the kinds of characteristic parameters so that slightest 
differences among the phonemes can be detected; 
(ii) Prepare standard patterns to emphasize consonant portions of the 
phonemes: and 
(iii) Improve the matching method so that it is less affected by the 
deformed phonemes. 
None of the above methods, however, have produced satisfactory results. 
SUMMARY OF THE INVENTION 
The object of the present invention is to provide a pattern recognition 
method which is capable of properly recognizing even confusing patterns 
based upon the above-mentioned facts, i.e., to provide a pattern 
recognition method which eliminates the above-mentioned first and second 
problems and in recognizing phonemes in order to enhance the recognition 
factor of the voice patterns. 
In order to accomplish the above object, according to the present 
invention, the standard pattern of the highest certainty obtained by the 
matching of an unknown pattern with the standard pattern, is decided by 
utilizing the matching results of other standard patterns inclusive of 
resembling patterns as recognized information, in order to reduce 
erroneous recognition and to increase the recognition factor. 
In accordance with the method of the invention, an input pattern is 
compared with standard patterns to produce identified values of each 
comparison of the input pattern with the standard patterns, a plurality of 
candidates are selected that are likely to be the input pattern based upon 
the identified values; and an input pattern is inferred based upon a 
predetermined criterion of inference. The predetermined criterion of 
inference is different than the criteria for selecting the plurality of 
candidates and utilizes the nature of the selected candidates and the 
commonness of each of the selected candidates with the other candidates. 
In accordance with the invention, there are four preferred methods for 
determining the criterion of inference. 
The principle of the present invention will be described below with 
reference to phoneme recognition based upon the pattern matching method. 
In general, phonemes are not totally unrelated to each other, and there are 
predetermined relationship among the phonemes. Therefore, the phonemes can 
be classified into several groups depending upon their common natures. 
According to the above classifications, the phonemes belong to several 
groups depending upon the natures. According to the results of recognition 
experiments conducted by the inventors of the present invention, the 
following facts were ascertained: 
(a) A distance obtained by comparing a phoneme group having a common nature 
with the standard pattern is smaller than a distance obtained by comparing 
a phoneme group without a common nature with the standard pattern. 
(b) Since each phoneme has a small amount of information, even a slight 
deformation causes the distance which is the result of the comparison to 
be greatly varied. There is, however, a predetermined upper limit in the 
distance, and the distance seldom varies in excess of the upper limit. 
(c) When priority is given to the phonemes depending upon their distances 
such that the phoneme having a minimum distance as a result of the 
comparison is entitled to the first order in certainty, the phonemes 
having the highest order of certainty have, in many cases, a common nature 
to the phonemes that pertain to the same category, even when the order of 
phonemes pertaining to the category which is the same as the standard 
pattern is reversed relative to the order of phonemes that pertain to a 
different category. Conversely, the phonemes without a common nature often 
have small orders in certainty. 
Relying upon these facts, the fundamental principle of the present 
invention consists of classifying the phonemes having higher orders in 
certainty as determined by the comparison into a plurality of groups 
depending upon their common natures, and specifying the phonemes that 
commonly belong to these groups as the input phonemes. 
In this case, it is possible to increase the precision of recognition 
depending upon whether the phonemes having less commonness to other 
phonemes are located at higher positions in certainty or not. 
What should be set and how it should be set as a common nature for 
classifying the phonemes will differ depending upon the characteristic 
parameters employed for the recognition and the language being discussed. 
However, a relatively stable classification is realized based upon the 
following natures: 
(1) Place of articulation, 
(2) Manner of production. 
However, the manner of production of the sound of the [g] series of 
Japanese language may be either /g/ (voiced stop consonant) or /.eta./ 
(nasal consonant). Therefore, the classification based upon the 
above-mentioned nature is not satisfactory. 
In specifically constructing an apparatus according to the invention, 
therefore, the phonemes should be classified depending upon the nature 
which is determined based upon a language or representative parameters.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Embodiments of the invention will be described below in detail with 
reference to specific data. 
First, a registered unit of a standard pattern is set to be 
vowel--consonant--vowel (a so-called VCV unit). This unit, however, need 
not be limited to the VCV unit provided it is lower than a level of 
linguistic signs of voices such as syllables and phonemes. 
If now a word (/atataka/) is fed as an input voice, there will exist the 
following distances from the first place to the sixth place as the result 
of comparing with various VCV's that are prepared as standard patterns for 
recognizing the second underlined consonant /t/. 
##EQU1## 
From the above results, the consonant in the input voice according to a 
conventional method will be erroneously recognized as /k/ of .circle.1 
which gives a minimum distance. The present invention provide a method 
which precludes the above defect, and extracts a first candidate /t/ as 
the correct answer from /ata/ which is in the fourth place from the 
viewpoint of distance. 
According to the results of a recognition experiment conducted by the 
inventors of the present invention, the distance in the VCV that may be a 
correct answer does not become greater than a minimum distance in all 
VCV's by more than 0.2, when the sampling frequency of the input voice is 
8 KHz, the Hamming window in the continuous non-linear matching (usually 
referred to as DP matching) is 20 msec., and the frame distance is 10 
msec. In the above-mentioned example, based upon this result, VCV's (six 
distances .circle.1 to .circle.6 in the relation (1)) serve as 
candidates of recognition having distances smaller than, 
EQU 1.53+0.3=1.83 
which is not greater, by more than +0.3, than a minimum distance 1.53 
(distance .circle.1 in the relation (1)). 
According to the first method of the present invention, consonants 
(including consonant /t/ of correct answer) in the six VCV's extracted as 
candidates of recognition are examined for their commonness. 
Therefore, the following facts can be understood. 
.circle.i The /k/ and /p/ which are voiceless stop consonants, are in 
agreement with each other in their manner of production, and belong to the 
same group. 
.circle.ii The /d/, /z/ and /s/ have a point of articulation at the tip 
of tongue, and are in agreement with each other in regard to their place 
of articulation, and belong to the same group. 
FIG. 1 shows six consonants which are candidates from the viewpoint of the 
manner of production and the place of articulation, consonants which can 
be classified into the same group, and the total number (N) in each group. 
According to FIG. 1, there are the greatest number of consonants that can 
be classified into the same group as the consonant /t/ of the correct 
answer. There are two consonants from the viewpoint of the manner of 
production, and three consonants from the viewpoint of the place of 
articulation. The total number N inclusive of /t/ is 6. 
Therefore, if the voice which is introduced is inferred with the magnitude 
of N as a criterion for inference, it is possible to obtain a correctly 
recognized result. 
Next, in order to enhance the precision of recognition, new distances 
reflecting the classified results of FIG. 1 are found from the distances 
that are obtained by the comparison, and voices that are introduced are 
inferred with the thus found distances as criteria for inference. 
Referring to the relation (1), if a distance of the i-th order is denoted 
by d.sub.i, a minimal value among d.sub.1 to d.sub.6 is denoted by 
d.sub.min (1.53 of /aka/), the number of consonants of the i-th order that 
pertain to the same group of FIG. 1 by N.sub.i, and distances of VCV's 
corresponding to N.sub.i consonants by d.sub.ij (j=1, 2 . . . N.sub.i) (in 
the case of /k/, for example, 1.53 of d.sub.11 =/aka/, 1.64 of d.sub.12 
=/ata/, and 1.65 of d.sub.13 =/apa/ when i=1 and N.sub.1 =3), the 
following new distance d.sub.1 ' can be defined responsive to the distance 
of the i-th order of the relation (1). 
EQU d.sub.1 '=w.sub.1 .multidot.w.sub.2 .multidot.w.sub.3 (2) 
Here, w.sub.1 denotes a weighing quantity which represents increased result 
of recognition with the increase in the number of consonants that pertain 
to the same group. For instance, 
EQU w.sub.1 =1/N.sub.i (3) 
Symbol w.sub.2 denotes a weighing quantity which represents increased 
result of recognition with the decrease in the distances that are results 
of comparisons. For instance, 
EQU w.sub.2 =1+d.sub.i -d.sub.min (4) 
Symbol w.sub.3 denotes a weighing quantity which represents an increased 
result of recognition with the decrease of distances that are results of 
comparisons relative to VCV's that pertain to the same group. For 
instance, 
##EQU2## 
The distance d.sub.i ' (i=1, 2, . . . 6) of the equation (2) is calculated 
using weighing quantities w.sub.1 to w.sub.3 given by the equations (3) to 
(5), and are indicated as follows in the order corresponding to .circle.1 
to .circle.6 of the equation (1). 
##EQU3## 
The distance d.sub.4 ' corresponding to /ata/ that serves as a correct 
recognition result assumes a minimal value 0.30. This verifies the 
effectiveness of the first method of the present invention. 
According to the results of a recognition experiment conducted by the 
inventors of the present invention, the recognition factor of 95% can be 
achieved by using the distance d.sub.i ' of the present invention compared 
with the recognition factor of 78% of the conventional method. 
In the above description, it was presumed that the number of VCV's 
belonging to the same group is nearly equal in all of the VCV's. Some 
VCV's, however, may belong to the same group in reduced numbers. 
With regard to such VCV's, the weight (w.sub.1 of the equation (3)) based 
on the number of VCV's belonging to the group is modifed and is balanced, 
or the modification is effected depending upon whether there is any 
candidate having a different nature among those classified into the same 
group as candidates of recognition. As for the candidate having a 
different nature, the weighing quantity corresponding to the equations (3) 
to (5) and the distance d.sub.i " corresponding to d.sub.i ' of the 
equation (2) are found depending upon the nature of the candidate, and the 
modification is effected depending upon the ratio d.sub.i '/d.sub.i ". 
If now the likelihoodration is used, the VCV close to the average spectral 
characteristics tends to appear as a candidate of recognition for various 
VCV's and also loses the likelihoodration value correspondingly. However, 
since the VCV having a great deviation feature appears as a candidate only 
for specific groups, it is possible to modify the distance d.sub.i 
beforehand by utilizing the above-mentioned nature. 
The above description has dealt with the method in which the degree of 
commonness is expressed in two steps, i.e., "1" (common) or "0" (not 
common), and the consonant /k/ of FIG. 1 has commoness to consonants /t/ 
and /p/ in regard to the manner of production and, hence, has a similarity 
degree 1, and has no commonness to other consonants /d/, /z/ or /s/ in 
regard to either the manner of production or the place of articulation 
and, hence, has a similarity degree 0. In other words, the above 
description has dealt with the method which equally handles the objects of 
recognition that belong to the same group relying upon the common nature. 
Below is mentioned a second method according to the present invention, in 
which the common nature is expressed by any numerical value between 0 and 
1 depending upon the degree of commonness to fairly evaluate the 
commonness among the phonemes, and to correct the deviation in the number 
of similar phonemes. 
First, the similarity degrees P.sub.IJ between the phonemes I in the input 
voices that are to be recognized and the phonemes J in the standard 
patterns, are found and are tabulated. The similarity degrees P.sub.IJ may 
be prepared relying upon the quantities phonemically defined based on 
common terms of discriminated features, or may be prepared utilizing the 
results of checking in the apparatus for recognizing the voice. 
FIG. 2 tabulates specific examples of quantities corresponding to the 
similarity degree P.sub.IJ. In this case, when I=J is denoted by 1, values 
within a range of 0 to 1 are rounded to 0.0, 0.2, 0.4, 0.6, 0.8 or 1.0, 
and the results are multiplied by 100. 
The similarity degree P.sub.IJ is a quantity which represents the degree of 
similarity between I and J. Therefore, (1-P.sub.IJ) can be regarded as a 
quantity which represents the degree of non-similarity between I and J. 
The unknown voice which is introduced is now denoted by I, and is matched 
to the standard pattern J to utilize L distances that have the greatest 
similarities (in the following description, the similarity is defined by 
the distance d.sub.IJ, the smaller the distance d.sub.IJ the greater the 
similarity), i.e., to utilize L distances that lie inside a predetermined 
threshold value. If these distances are denoted as follows in the order of 
increasing quantities, 
EQU d.sub.I1, d.sub.I2, d.sub.I3, . . . , d.sub.IL (7) 
the unknown voice I which is introduced will be specified as the one among 
1 to L. 
In inferring that the unknown voice is I based upon these quantities, the 
precision of inference can be increased through the following processing. 
First, if 
##EQU4## 
is calculated, S.sub.I becomes a quantity that indicates a degree which 
does not mean that the input voice is I. 
Moreover, the distance d.sub.IJ which is increased serves as a quantity 
that indicates an increasing degree at which I is not J. 
Therefore, if S.sub.I and d.sub.IJ are combined together to define. 
##EQU5## 
it is considered that d.sub.I ' becomes a quantity that indicates a degree 
at which the unknown voice is not I. By using this quantity as a criterion 
of inference, it is possible to infer the voice to be I.sub.O when, 
EQU d.sub.IO '=M.sub.in [d.sub.1 ', d.sub.2 ', d.sub.3 ', . . . d.sub.L '] 
The distance d.sub.I ' calculated according to the equation (9) corresponds 
to d.sub.i ' of the equation (2). When the weighing quantity w.sub.3 of 
the equation (2) is found, however, the distances, 
EQU d.sub.i1, d.sub.i2, d.sub.i3, . . . d.sub.iNi 
which are the candidates are all equally treated as given by the equation 
(5). 
According to the equation (9), on the other hand, the weighing (1-P.sub.IJ) 
is effected for all of the candidate distances, 
EQU d.sub.I1, d.sub.I2, d.sub.I3, . . . , d.sub.IL 
depending upon the similarity between I and J (J=1, 2, . . . , L) to find 
the distance d.sub.I which is weight averaged. Therefore, it is possible 
to find a distance which more faithfully reflects the distance relative to 
the standard pattern. 
In the case of the input voice I having small number of similar phonemes, 
the number of candidates L is small as given by the equation (7), and the 
distance d.sub.I ' is generally large, making it difficult to perform 
correct recognition. 
To correct this, a correction coefficient C.sub.I for the distance d.sub.I 
' is introduced to define. 
##EQU6## 
and using the above quantity as a criterion of inference, the voice is 
inferred to be I.sub.O based upon a relation, 
EQU d.sub.IO "=M.sub.in [d.sub.1 ", d.sub.2 ", d.sub.3 ", . . . d.sub.L "] 
For example, the correction coefficient C.sub.I is calculated as follows 
(numerical values are specifically shown in the bottom row of FIG. 2) 
based upon P.sub.IJ that corresponds to 1/100 of the numerical values of 
FIG. 2, 
##EQU7## 
where M denotes the total number of the standard patterns which are 
prepared. 
In the case of the phonemes having large C.sub.I values, there exist a lot 
of similar phonemes, and the distance d.sub.I ' of the equation (9) tends 
to become small. Therefore, use of the distance d.sub.I " corrected by 
C.sub.I enables the phonemes to be fairly recognized. 
According to the recognition experiments conducted by the inventors of the 
present invention, nine objects were erroneously recognized among about 
100 objects when the distance d.sub.IJ was employed. When the distance 
d.sub.I ' was employed, four objects were erroneously recognized. Further, 
when the distance d.sub.I " was employed, only one object was erroneously 
recognized. 
FIG. 3 shows the results of recognition using the distances d.sub.I ' and 
d.sub.I " for the four consonants of which the distance d.sub.IJ usually 
ranges from the first order to the fourth order from the smaller side in 
case the input voice to be recognized is a consonant /s/. 
In FIG. 3, the consonant is correctly recognized as /s/ when d.sub.I " is 
used, even though it may be erroneously recognized as /t/ or /z/ when 
d.sub.IJ or d.sub.I ' is used. 
According to the above two methods, part of the standard pattern prepared 
based upon the compares values is selected as a candidate for recognition, 
and an unknown pattern is inferred from the candidates relying upon a 
predetermined criterion of inference. 
A third method of the present invention will be described below, using a 
criterion of inference extracted from the combined information of input 
pattern and a plurality of standard patterns. 
If an input pattern is denoted by i, a standard pattern by j, a degree of 
similarity corresponding to a compared value of the input pattern i and 
the standard pattern j by d.sub.i,j, the appearing probability of the 
input pattern i by p(i), the probability in which the similarity degree 
between the input pattern i and the standard pattern j is d.sub.i,j by 
p(d.sub.i/j /i, j), the probability in which the input pattern is i when 
the similarity degree is d.sub.i,j by p(i.vertline.d.sub.i,j), and the 
probability in which the input pattern i is compared with the standard 
pattern j is denoted by p(i, j), the comparison of the input pattern i 
with the standard pattern j indicates that the probability p(i.vertline.i, 
j) in which the input pattern i comes into agreement with the standard 
pattern j, is given by 
EQU p(i.vertline.i, j)=p(i).multidot.p(i,j).multidot.p(d.sub.i,j 
.vertline.i,j).multidot.p(i.vertline.d.sub.i,j) (12) 
According to the conventional method, j is presumed to be equal to i, and 
the input pattern is specified by i which satisfies. 
##EQU8## 
According to the third method of the present invention, on the other hand, 
the input pattern is specified by i which maximizes a relation, 
##EQU9## 
where N denotes the total number of standard patterns, using 
##EQU10## 
as a criterion of inference. 
The probability p(i) can be statistically determined from the distribution 
of patterns. For example, the phonemes of the Japanese Language can be 
recognized by utilizing the results of investigation concerning the 
frequency of phonemes. 
When all of the standard patterns and input patterns are compared, p(i, 
j)=1/N. The probability p(d.sub.i,j .vertline.i,j) and the probability 
p(i.vertline.d.sub.i,j) can be determined by defining the practical 
characteristic parameters and similarity degrees, and by observing the 
distribution of the data, correspondingly. The distribution of d.sub.ij 
differs depending upon the parameters and the similarity degree. When i=j, 
in particular, the distribution often becomes asymmetrical with respect to 
an average value d.sub.ij of d.sub.ij. In many cases, however, the 
distribution is symmetrical and can be approximated by the normal 
distribution. Therefore, it is virtually convenient to normalize the 
distribution with a dispersion .sigma..sub.i,j to treat it as a function 
of 
##EQU11## 
Therefore, if 
EQU p(d.sub.i,j .vertline.i,j).multidot.p(i.vertline.d.sub.i,j) 
is approximated with the normal distribution like, 
##EQU12## 
the value of the equation (15) increases with the decrease in 
.delta..sub.i, j. Therefore, the object which takes the sum of the 
equation (14) may be limited to the number n of combinations of i and j 
having a small value .delta..sub.ij (in this case, the equation (14) is 
treated with regard to values n smaller than the total number N). When the 
likelihoodration or a square distance is to be used as a similarity 
degree, a value among patterns having small similarity undergoes great 
change even for a slight change in the patterns, and becomes unstable. Due 
to this unstability factor, therefore, the value .sigma..sub.ij becomes 
great and an apparent value .delta..sub.ij becomes small. In such a case, 
the objects which assume the sum of the equation (14) are not simply 
limited to those having small value .delta..sub.ij but the value d.sub.ij 
itself is limited to those having increased certainty (or having small 
likelihoodration or distance). Even in this case, the equation (14) is 
executed for the output that corresponds to n standard patterns having 
values smaller than the total number N. Thereafter, the total number N 
includes the meaning of n of such a meaning. 
Accordingly, it is possible to specify the input pattern using i which 
approximately assumes, 
##EQU13## 
instead of the equation (14). Furthermore, if 
##EQU14## 
and the equation (16) is given by, 
##EQU15## 
there is no need of effecting the division. 
Discussed below is a modification method based upon the idea of a matching 
method according to the above-mentioned third method utilizing the 
information consisting of a combination of i and j. The equation (17) is 
modified as follows: 
##EQU16## 
where w denotes the weight, and a.sub.ij and c.sub.O denote constants. 
Here, a.sub.ij is defined as follows: 
EQU a.sub.ij =c.sub.ij -c.sub.O (19) 
with the average value of d.sub.ij as c.sub.ij (c.sub.ij =d.sub.ij). The 
constant c.sub.O is so determined that d.sub.ij does not usually become 
greater than it when the input pattern i and the standard pattern j have 
commonness with regard to some nature, and that d.sub.ij does not become 
smaller than it when the input pattern i and the standard pattern j do not 
have commonness. If the constant c.sub.O is determined as mentioned above, 
a.sub.ij (c.sub.O -d.sub.ij) in the equation (18) assumes a negative value 
in most cases when the input pattern i and the standard pattern j have 
commonness in regard to some nature, and assumes a positive value in most 
cases when there is no commonness between i and j. Therefore, the second 
term of the equation (18), i.e., 
##EQU17## 
works to correct the result d.sub.ij of the j-th matching portion 
depending upon the degree of commonness to the result d.sub.ij of other 
matching portions. In particular cases, it is allowable to set that 
a.sub.ij =0. In this case, operation for the correction term for the 
combination can be eliminated to reduce the quantity of operation. When 
the phonemic commonness is very small, the value d.sub.ij will often 
become unstable. For such combinations, therefore, the value a.sub.ij 
should be set to 0 beforehand to obtain stable results. Further, the value 
d.sub.ij which is greater than a predetermined level will not be reliable. 
Therefore, it is better not to use the term thereof. 
Described below is a further specific illustration of the principle of the 
third method when it is adapted for recognizing voices, particularly for 
recognizing phonemes in continuous voice. 
FIG. 4 is a block diagram of the apparatus for recognizing voice based upon 
the above-mentioned principle. FIG. 4 principally illustrates a matching 
portion which executes the operation of the equation (14) to illustrate 
the principle of the third method of the present invention, and shows the 
flow of signals. The input voice 1 is converted into characteristic 
parameters through an analyzing circuit 2, and is sent to identifying 
circuits 3-1 to 3-N for checking with standard pattern memories 4-1 to 4-N 
of each of the phonemes. Results 5-1 to 5-N of checking or identification 
with the phonemes are sent to matching circuits 6-1 to 6-N. Utilizing the 
results 5-1 to 5-N of checking with the phonemes, matching circuits 6-1 to 
6-N perform calculations corresponding to each of the terms of the 
equation (14), whereby results 7-1 to 7-N are sent to a discriminating 
circuit 8. The discriminating circuit 8 compares the results, 
discriminates the phoneme having the highest degree of certainty, and 
produces a signal 9. 
A first system in the third method based upon the equation (4) is 
illustrated below. 
Likelihoodration of the tenth order in used as the degree of similarity. 
First, the registered unit of a standard pattern consists of 
vowel--consonant--vowel (a so-called VCV unit). This unit need not be 
limited to the VCV unit provided it is lower than a level of linguistic 
signs of voices such as syllable or phoneme. 
According to the results of recognition experiments conducted by the 
inventors of the present invention, a distance in the VCV that is a 
correct answer does not become greater than a minimum distance in all of 
the candidate VCV's by more than 0.2, when the sampling frequency of the 
input voice is 8 KHz, the Hamming window in a continuous non-linear 
matching (usually called continuously DP matching) using the dynamic 
programming method is 20 msec, and the distance among the frames is 10 
msec. Further, the distance seldom exceeds 2.0 in the VCV that serves as a 
correct answer. When 2.0 is exceeded, the distance should be rejected as 
it stems from unstable inputs. Therefore, the d.sub.ij which is not 
greater than those having the greatest certainty by more than 0.4 and 
which is smaller than 2.0, is used. Below are described the results 
d.sub.ij produced by the identifying circuits 3-1 to 3-N for /k/ after the 
input voice /Kagakuhooteishiki/. 
First place: /g/ 1.634 
Second place: /k/ 1.774 
Third place: /b/ 1.910 
Fourth place: /p/ 1.927 
In the equation (17), if a value d.sub.ij is measured as shown in FIG. 5, 
and if the dispersion .sigma..sub.ij is presumed to be 1, then, 
First place: /k/ 0.847/4 
Second place: /p/ 1.433/4 
Third place: /b/ 2.237/4 
Fourth place: /g/ 3.067/4 
Thus, /k/ becomes the first place. 
Below is mentioned a modified method based on the equation (18) as a second 
embodiment of the third method. 
When, 
First place: /g/ 1.634 
Second place: /k/ 1.774 
Third place: /b/ 1.910 
Fourth place: /p/ 1.927 
if C.sub.O =2.2, W=1.0, and C.sub.ij is given as shown in FIG. 5, d.sub.ij 
' after being corrected becomes: 
First place: /k/ 1.672 
Second place: /g/ 1.839 
Third place: /p/ 1.927 
Fourth place: /b/ 1.997 
and the correct answer /k/ takes the first place. 
Below is mentioned an apparatus for recognizing the voice according to the 
present invention with reference to the situation when the voice is to be 
recognized, particularly when the phoneme in the continuous voice is to be 
recognized. 
FIG. 6 is a block diagram of an apparatus for recognizing the voice 
according to an embodiment of the present invention. 
In FIG. 6, an input voice 61 passes through a lowpass filter (LPF) 62 for 
preventing aliasing noise, and is converted into digital signals through 
an analog-to-digital converter (ADC) 63. Then, a conventional 
characteristic parameter analyzing circuit 64 produces a frame data 
consisting of a short-term autocorrelation [v.sub.i ] and a residual power 
P.sub.O as a characteristic parameter after every interval of one frame 
(for example, 10 msec.). 
Likelihoodration which represents the similarity between a series of frame 
data and a series of frame data of standard patterns stored in a standard 
pattern memory 66, is calculated by a likelihoodration calculating circuit 
65. 
Based upon the thus calculated likelihoodration, an optimum identified 
value is processed by a conventional continuous DP matching circuit 67 via 
an intermediate result memory 68, thereby to calculate the distance 
[d.sub.IJ ]. 
The distance [d.sub.IJ (J=1, 2, . . . )] is fed to a phoneme identified 
value processing circuit 600 via a buffer 69 where the recognition 
processing is carried out according to the method of the present 
invention, and a final result 610 of the processing of phoneme recognition 
is produced. 
Here, and phoneme identified value processing circuit 600 may be made up of 
an ordinarily used microprocessor. When the first and second methods of 
the present invention are to be carried out using the microprocessor, 
however, portions surrounded by a dotted line are executed as shown in the 
flow chart of FIG. 7. Further, when the third method of the present 
invention is to be performed, the processing is carried out as shown in a 
flow chart of FIG. 8. 
The foregoing description has employed likelihoodration as a scale for 
measuring the similarity. Therefore, the circuits subsequent to the 
continuous DP matching circuit 67 in FIG. 6 perform such a processing that 
the certainty increases with the decrease in the value. The same also 
holds true even when the distance is used as a scale for measuring the 
similarity. 
When the correlation is to be used, however, the processing must be carried 
out in a way that the certainty increases with the increase in the value. 
For example, the reliability must be increased with the increase in the 
weighing quantities w.sub.1, w.sub.2 and w.sub.3 in the equation (2). The 
present invention naturally includes these modifications. 
According to the present invention as illustrated in the foregoing, the 
voice such as phonemes can be stably and precisely recognized on a level 
lower than a linguistic level of signs, presenting great effects.