Lombard effect compensation using a frequency shift

An analyzer performs spectral analysis of a characteristic parameter of an input speech signal per unit time so as to calculate, as a recognition parameter, orthogonal expansion of a spectrum of the analyzed characteristic parameter. A formant frequency of the input speech signal is detected, and a compensation value is calculated for the recognition parameter using the formant frequency. The recognition parameter is compensated in accordance with variations of the input speech signal caused by a noisy environment using the compensation value. The compensated recognition parameter is compared with reference parameters to output a recognition result.

BACKGROUND OF THE INVENTION 
The present invention relates to a speech recognizer. 
In response to recent developments in speech recognition technology, speech 
recognizers are almost ready to be put to practical use in various fields. 
However, in order to achieve such practical use of speech recognizers, 
many problems must yet be solved. 
In actual use, the operating states in which the speech recognizers are 
used vary, thus causing variations in the voice patterns of speech to be 
recognized. For example, when background noise in the vicinity of the 
speech recognizers becomes significant, a speaker must speak loudly, 
thereby resulting in variations in voice patterns. Such variations in 
voice patterns caused by a speaker attempting to overcome a noisy 
environment are called the "Lombard effect". The voice patterns also vary 
when the speech recognizers are used for a long time and the speaker 
becomes tired. Furthermore, when the speakers themselves are changed, the 
voice patterns vary. 
Therefore, if a state in which a speech recognizer has learned a reference 
voice pattern is different from a state in which the speech recognizer is 
used, a serious problem arises in that the reference voice pattern cannot 
account for the above mentioned variations in voice patterns, thereby 
resulting in erroneous speech recognition. 
In order to solve this problem, a countermeasure is employed in which the 
speech recognizer is made to learn all foreseeable voice pattern 
variations in advance. However, such is not practical in that the learning 
time and capacity of the speech recognizer must be increased enormously 
and the operator must perform extremely troublesome operations. 
Thus, in recent years, a method has been proposed in which voice pattern 
variations are calculated each time the speech recognizer is used and 
analytical conditions are changed in accordance with the voice pattern 
variations at the time of analyzing characteristic parameters of voice 
pattern variations. By employing this method, voice pattern variations can 
be accounted for using a shorter learning time and smaller speech 
recognizer capacity and requiring a lesser burden on the operator. 
Hereinbelow, a known speech recognizer is described with reference to FIG. 
1. The known speech recognizer is of a registration type in which the 
reference voice pattern is made by inputting the voice of a user. As one 
example in which voice patterns at the time of storing a reference voice 
pattern and at the time of voice pattern recognition differ from each 
other, a case where noise in the surrounding environment varies is 
adopted. In FIG. 1, the known speech recognizer includes a signal input 
terminal 1, a power measuring portion 20, analyzers 21 and 23, a vowel 
deciding portion 22, a matching portion 8, and output terminal 9 for 
outputting a recognition result, a buffer 10 for storing the reference 
voice pattern and switches 24, 25 and 26. 
The known speech recognizer of the above described arrangement is operated 
as follows. Initially, at the time of storing the reference voice pattern, 
environmental noise in the vicinity of the speech recognizer, immediately 
before the input of the reference voice signal, is input to the signal 
input terminal 1 and the power level of the environmental noise is 
calculated by the power measuring portion 20. If the power level of the 
environmental noise exceeds a predetermined threshold value P1, the 
environment is regarded as being unsuitable for storing of the reference 
voice pattern and thus, registration of the reference voice pattern is 
suspended. On the contrary, if the power of the environmental noise is not 
more than the threshold value P1, a reference voice pattern signal is 
input to the signal input terminals 1 and is fed to the analyzer 21 where 
a characteristic parameter is calculated. At this time, the input signal 
is passed through a filter F1 expressed by the following equation (i). 
EQU F1(z)=1-0.9375.times.Z.sup.-1 (i) 
In the equation (i), character Z denotes the Z-function of the FFT (fast 
Fourier transformation) X(f) for transforming a time function into a 
frequency function. Assuming that character t denotes time and character f 
denotes frequency, the FFT X(f) is given by: 
##EQU1## 
where exp(-j2.pi.ft) is expressed by the Z-function Z, i.e. 
Z.ident.exp(-j2.pi.ft). 
After a high-frequency band of the input signal has been emphasized by the 
filter F1, the input signal is analyzed. If the LPC cepstrum method is 
carried out in the analyzer 21, a predetermined number of LPC cepstral 
coefficients are calculated as characteristic parameters. When the Dower 
level of the voice pattern exceeds a detection threshold value within a 
predetermined voice pattern interval, the corresponding characteristic 
parameter is regarded as the reference voice pattern to be stored in the 
buffer 10. The above described processing starting from the input of the 
reference voice pattern signal is performed for all words to be recognized 
and the registration process is then complete. 
Subsequently, at the time of speech recognition, the power level of 
environmental noise is measured in the same manner as in the reference 
voice pattern registration process and then, a voice signal is input via 
the signal input terminal 1. If the power level of the environmental noise 
is not more than the threshold value P1, a characteristic parameter of the 
input voice signal is calculated using the analyzer 21 in the same manner 
as in the registration process, and the thus calculated characteristic 
parameter is transmitted to the matching portion 8. At the matching 
portion 8, the variations between the reference voice patterns and the 
input voice pattern is calculated and a word exhibiting a minimum 
variation is output as a recognition result from the output terminal 9. 
On the other hand, if the power level of the environmental noise exceeds 
the threshold value P1, the power level of the input voice signal is 
calculated for each frame by the power measuring portion 20 and then, the 
power level of the environmental noise and the power of the input voice 
signal are fed to the vowel deciding portion 22. At the vowel deciding 
portion 22, a vowel determination is made based on the following 
conditions (a) and (b). 
(a) The signal level is higher than a sum of the noise level and a constant 
C. 
(b) Five or more continuous frames satisfying the above condition (a). 
It is determined that a frame which satisfies the conditions (a) and (b) is 
a vowel. If a frame is not determined to be a vowel, the input signal is 
fed to the analyzer 21, a high-frequency band of the frame is emphasized 
using the filter expressed by the above equation (i) and the 
characteristic parameter is calculated in the same manner as in the case 
of the reference voice pattern registration process. On the other hand, if 
a frame is so determined to be a vowel, the input signal is fed to the 
analyzer 23, a high-frequency band of the frame is emphasized by a filter 
F2 expressed by the following equation (ii). 
EQU F2(Z)=1-0.6375.times.Z.sup.-1 (ii) 
The emphasis of the high-frequency band of the frame by the filter F2 is 
less than that of the filter F1 and the tilt of the equation (ii) is 
milder than that of the equation (i). When environmental noise becomes 
large, the voice state of a speaker changes such that a high-frequency 
band of the input voice signal becomes intense. Therefore, the tilt of a 
filter for emphasizing a high-frequency band in a noisy environment is 
required to be milder than that in a less noisy environment. After the 
input voice signal has been passed through the filter F2, the 
characteristic parameter thereof is calculated in the same manner as in 
the reference voice pattern registration process. 
The calculated characteristic parameter is fed to the matching portion 8 
and the recognition result is generated from the output terminal 9 in the 
same manner as in the case where the power level of the environmental 
noise is not more than the threshold value P1. 
The switch 24 actuates to changed over to the vowel deciding portion 22 and 
to the analyzer 21 when the power level of the environmental noise exceeds 
the threshold value P1 and is not more than the threshold value P1, 
respectively. When no voice signal is being input, the switch 24 is in an 
OFF state. The switch 26 actuates to changed over to the analyzer 23 and 
to the analyzer 21 when the frame is determined to be a vowel and is not 
determined to be a vowel, respectively. Meanwhile, the switch 25 actuates 
to change over to the buffer 10 and to the matching portion 8 during the 
reference voice pattern registration process and voice recognition 
process, respectively. 
In the above described known speech recognizer, changes in spectral tilt 
due to variations of voice patterns are initially compensated for, and 
then the parameter used for recognition of a voice signal is analyzed. 
Thus, the known speech recognizer suffers drawbacks in that, since the 
contents of the compensation are not accurately incorporated into the 
parameter through the analysis processing, the compensation efficiency is 
reduced and in some cases, the compensation does not contribute to an 
improvement of the recognition rate at all. 
Furthermore, the known speech recognizer is disadvantageous in that, 
although it is possible to compensate for changes in spectral tilt, it is 
not possible to compensate for changes in the resonance frequency 
characteristic of a vocal sound (referred to as "formant frequency", 
hereinbelow) caused by variations of voice patterns, thereby resulting in 
a lowering of the recognition rate. 
SUMMARY OF THE INVENTION 
Accordingly, an essential object of the present invention is, with a view 
to eliminating the above described inconveniences inherent in the 
conventional speech recognizers, to provide a speech recognizer in which 
variations of voice signals uttered in a noisy environment are compensated 
for in a recognition parameter. In the present invention, spectral 
analysis is performed in an analyzer and orthogonal expansion of a 
spectrum is calculated as a recognition parameter. Furthermore, in the 
present invention, a compensation value is calculated in a compensation 
value calculator by using a formant frequency detected by a formant 
frequency detector, and the recognition parameter is compensated for in a 
parameter compensator by using a compensation value. 
In order to accomplish this object of the present invention, a speech 
recognizer according to the present invention comprises: an analyzer which 
performs spectral analysis of a characteristic parameter of an input 
signal per unit time so as to calculate, as a recognition parameter, an 
orthogonal expansion of a spectrum of the analyzed characteristic 
parameter; a formant frequency detector for detecting a formant frequency 
of the input signal; a compensation value calculator for calculating a 
compensation value of the recognition parameter by using the formant 
frequency; a parameter compensator for compensating for the recognition 
parameter in accordance with variations of the input signal by using the 
compensation value; and a matching portion which calculates the variation 
between reference parameters and input parameters by using the compensated 
recognition parameter so as to output a recognition result. 
In accordance with the present invention, changes of the formant frequency 
due to input voice variations, which have not been hitherto compensated 
for, can be efficiently compensated for directly on the recognition 
parameter, thereby resulting in a significant improvement in the 
recognition rate.

DETAILED DESCRIPTION OF THE INVENTION 
Referring now to the drawings, there is shown in FIG. 2 a word speech 
recognizer K1 according to a first embodiment of the present invention. 
The speech recognizer K1 includes a signal input terminal 1, a power 
measuring portion 2, a first analyzer 3, a vowel deciding portion 4, a 
formant frequency detector 5, a compensation value calculator 6, a 
parameter compensator 7, a matching portion 8, an output terminal 9 for 
outputting a recognition result, a buffer 10 for storing a reference voice 
pattern and switches 11 to 14. 
The operation of the speech recognizer K1 having the above described 
arrangement is described hereinbelow. Initially, at the time of storing 
the reference voice pattern, noise contained in the environment 
surrounding the speech recognizer K1, immediately before the input of 
voice signals, are input to the signal input terminal 1 and the power 
level of the environmental noise is calculated by the power measuring 
portion 2. If the power level of the environmental noise exceeds a 
predetermined threshold value P1, the environment is regarded as being 
unsuitable for the reference voice pattern registration, and thus the 
registration process is suspended. 
On the other hand, if the power level of the environmental noise is not 
more than the threshold value P1, a reference voice signal input to the 
signal input terminal 1 is fed to the power measuring portion 2 where the 
power level of each frame of the reference voice signal is calculated. 
Meanwhile, the input reference voice signal is also fed to the first 
analyzer 3 where a characteristic parameter of the reference voice signal 
is calculated. Analysis is performed in a known manner and an LPC cepstral 
coefficient having a predetermined order is calculated as a recognition 
parameter. The recognition parameter is not restricted to an LPC cepstrum 
and may also be an orthogonal expansion of a spectrum. However, since an 
LPC cepstrum is stable as the recognition parameter and its calculation is 
relatively easy, an LPC cepstrum is employed in this embodiment. The 
recognition parameter is a frame having a power level which exceeds a 
detection threshold value within a predetermined voice signal interval is 
stored in the buffer 10. The above described processing starting from the 
input of the reference voice signal is performed for all words to be 
recognized and the registration process is then complete. 
Subsequently, at the time of recognition of a voice signal, the power level 
of environmental noise is measured by the power measuring portion 2 in the 
same manner as in the reference voice signal registration process. A voice 
signal to be recognized is then applied to the signal input terminal 1 and 
an LPC cepstral coefficient of the input voice signal is calculated by the 
first analyzer 3 in the same manner as in the reference voice signal 
registration process. If the power level of the environmental noise is not 
more than the threshold value P1, the parameter of the input voice signal 
is input to the matching portion 8. At the matching portion 8, the 
variation between the reference voice signal parameter and the input voice 
signal parameter is calculated and a word exhibiting a minimum variation 
between the reference voice signal parameter and the input voice signal 
parameter is output as a recognition result from the output terminal 9. 
On the other hand, if the power level of the environmental noise exceeds 
the threshold value P1, the power level of each frame of the input voice 
signal is calculated by the power measuring portion 2, and then the power 
level of the environmental noise and the power level of the voice signal 
frames are fed to the vowel deciding portion 4. At the vowel deciding 
portion 4, a vowel determination is made based on the following conditions 
(a) and (b). 
(a) The signal level is higher than a sum of the noise level and a constant 
C. 
(b) Five or more continuous frames satisfy the above condition (a). 
A frame which satisfies the conditions (a) and (b) is determined to be a 
vowel. If it is determined that a frame is a vowel, the input voice signal 
is fed to the formant frequency detector 5. On the other hand, if it is 
determined that a frame is not a vowel, the input voice signal is fed to 
the matching portion 8. 
When a frame is determined to be a vowel, a formant frequency of the input 
voice signal is detected by the formant frequency detector 5 so as to 
compensate for the parameter of the input voice signal. The formant 
frequency is obtained as follows. In the course of the calculation of the 
LPC cepstrum acting as the recognition parameter in this embodiment, an 
LPC parameter a(i) is obtained. By using this LPC parameter a(i), an 
acoustic spectrum S is given by: 
##EQU2## 
where the character N denotes an order of analysis. From a real part Re(Z) 
and an imaginary part Im(Z) of a complex root of this A(Z), the formant 
frequency f is expressed by: 
EQU f=(fs/2.pi.) tan.sup.-1 [Im(Z)/Re(Z)] 
where the character fs denotes the sampling frequency. Thus, it becomes 
possible to obtain the formant frequency from the LPC parameter. 
Subsequently, at the compensation value calculator 6, a compensation value 
of the LPC cepstrum is calculated by using a product of the formant 
frequency and a value obtained by differentiating the LPC cepstrum by the 
formant frequency. Supposing that the formant frequency is fi Hz, a 
compensation amount H(fi, n) of the N-th cepstral coefficient is given by: 
##EQU3## 
where the character .DELTA.fi denotes a difference in the formant 
frequency between the presence and absence of variations of voice signals, 
then character Cn denotes n-th cepstral coefficient, character fi denotes 
the i-th formant frequency and the character (M/2) denotes the number of 
formant frequencies. 
The n-th cepstral coefficient Cn and the term (.delta.Cn/.delta.fi) are 
expressed by: 
##EQU4## 
where the character fs denotes the sampling frequency and the character bi 
denotes the bandwidth of the i-th formant frequency fi. 
For example, if compensation is performed for only the formant contained in 
a frequency range of 300-1,500 Hz and greatly changeable due to voice 
signal variations, in the case of fi=120 Hz, bi=150 Hz and fs=10 KHz, the 
equation (1) is converted into the following equation (2): 
##EQU5## 
where 300 Hz&lt;fi&lt;1500 Hz. 
The compensation value calculated by the equation (2) is input to the 
parameter compensator 7 where the LPC cepstral coefficient Cn is 
compensated for by the following equation (3). 
EQU Cn=Cn+H(fi,n) (3) 
The variation between the reference voice signal parameter and the input 
voice signal parameter is calculated in the matching portion 8 in the same 
manner as in the case where the power level of the environmental noise is 
not more than the threshold value P1. Then, the recognition result is 
output from the output terminal 9. 
Meanwhile, in this embodiment, the product between the shift of the formant 
frequency and the partial differential of the LPC cepstrum by the formant 
frequency is calculated as the compensation value using the equation (1). 
However, the compensation value is not restricted to this scheme. For 
example, without using the partial differential, identical effects can be 
achieved by using a value indicative of tilt relative to the formant 
frequency. Meanwhile, generally in the case where orthogonal expansion of 
the spectrum is employed as the recognition parameter without using the 
LPC cepstrum, identical effects can be obtained by using either the 
transformation nucleus of the recognition parameter or the tilt of a 
periodic function having a period, a phase and a sign which are identical 
to those of the transformation nucleus. However, when the LPC cepstrum is 
employed as the recognition parameter, the compensation amount can be 
obtained easily and accurately using the equation (1). Thus, in this 
embodiment, the equation (1) is employed. 
Meanwhile, the switch 11 actuates to change over to the power measuring 
portion 2 and to the first analyzer 3 at a time when the power level of 
the environmental noise, immediately before the input of the voice signal, 
is measured and at a time when the voice signal is input, respectively. 
The switch 12 is changed over to the buffer 10 at the time of the 
reference voice signal registration process and is changed over to the 
matching portion 8 or the vowel deciding portion 4 at the time of the 
voice signal recognition process. The switch 13 actuates to change over to 
the matching portion 8 and to the vowel deciding portion 4 at a time when 
the power level of the environmental noise is not more than the threshold 
value P1 and at a time when the environmental noise exceeds the threshold 
value P1, respectively. Meanwhile, the switch 14 actuates to change over 
to the formant frequency deciding portion 5 and to the matching portion 8 
when the input voice signal is a vowel and is not a vowel, respectively. 
As described above, in this embodiment, the LPC cepstral coefficient is 
calculated as the recognition parameter in the first analyzer and the 
formant frequency of the inputted signal is detected in the formant 
frequency detector. Furthermore, the compensation value is calculated in 
the compensation value calculator by using the equation (1) and the 
recognition parameter is compensated for in the parameter compensator by 
adding thereto the above compensation value in accordance with changes of 
the formant frequency of the input voice signal. Accordingly, contents of 
the compensation are accurately incorporated into the recognition 
parameter, thereby resulting in a improvement in the compensation 
efficiency. Furthermore, since it is possible to compensate for changes of 
the formant frequency due to voice signal variations, the recognition rate 
can be improved. 
In this embodiment, voice signal variations in a noisy environment has been 
described. However, in voice signal variations due to other causes, the 
present invention is applicable to compensation for changes of voice 
signals in which the formant frequency varies. Compensation using the 
equation (1) is most effective for the case in which only limited formant 
frequencies vary under certain rules. In the case of utterance of voice 
signals in a noisy environment, only formant frequencies ranging from 
about 300 to 1,500 Hz rise, which satisfies the above described condition 
for the most effective compensation. Accordingly, compensation of the 
present invention is especially effective for variations of voice signals 
in a noisy environment. 
FIG. 3 shows a word speech recognizer K2 according to a second embodiment 
of the present invention. The speech recognizer K2 includes a power 
measuring portion 15, a shift estimator 16 and a compensation value 
calculator 17. Since other elements of the speech recognizer K2 are 
similar to those of the speech recognizer K1, the description thereof has 
been abbreviated for the sake of brevity. 
The operation of the speech recognizer K2 having the above described 
arrangement is described hereinbelow. Initially, at the time of 
registration of the reference voice signal, noise contained in the 
environment surrounding the speech recognizer K2, immediately before the 
input of the reference voice signals, are input to the signal input 
terminal 1 and the power level of the environmental noise is calculated by 
the power measuring portion 15. If the power level of the environmental 
noise exceeds the predetermined threshold value P1, the environment is 
regarded as being unsuitable for registration of the reference voice 
signal and thus the reference voice signal registration process is 
suspended. 
On the other hand, if the power level of the environmental noise is not 
more than the threshold value P1, a reference voice signal is input to the 
signal input terminal 1 and fed to the first analyzer 3 where a 
characteristic parameter of the reference voice signal is calculated. 
Analysis is performed in a known manner and an LPC cepstral coefficient 
having a predetermined order is calculated as a recognition parameter. The 
characteristic parameter in a frame in which the power exceeds a detection 
threshold value within a predetermined voice signal interval is stored in 
the buffer 10. The above described processing starting from the input of 
the reference voice signal is performed for all words to be recognized and 
the registration process is then complete. 
Subsequently, at the time of voice signal recognition, the power level of 
the environmental noise is measured by the power measuring portion 15 in 
the same manner as in the case of reference voice signal registration 
process. A voice signal to be recognized is then applied to the signal 
input terminal 1 and a recognition parameter of the input voice signal is 
calculated by the first analyzer 3 in the same manner as in the reference 
voice signal registration process. If the power level of the environmental 
noise is not more than the threshold value P1, the parameter of the 
inputted voice signal is input to the matching portion 8. In the matching 
portion 8, the variation between the reference voice signal parameter and 
the input voice signal parameter calculated and a word exhibiting a 
minimum variation between the reference voice signal parameter and the 
input voice signal parameter is output as a recognition result from the 
output terminal 9. 
On the contrary, if the power level of the environmental noise exceeds the 
threshold value P1, the power level of the environmental noise is input to 
the shift estimator 16. At the shift estimator 16, the shift f of the 
formant frequency due to the Lombard effect, i.e. variations in voice 
signals uttered in a noisy environment, is calculated from a power Pn of 
the environmental noise using the following equation (4): 
EQU .DELTA.f=10 Hz/db.times.(Pn-P1) (4) 
where the power levels Pn and P1 are expressed in units dB and the shift 
.DELTA.f is expressed in units of Hz. 
The equation (4) represents that, in the case where the environmental noise 
is small, variations in voice signals are reduced and thus the shift of 
the formant frequency is also small, while in the case where the level of 
environmental noise is large, variations in voice signals are increased 
and thus the shift of the formant frequency is also large. 
Subsequently, a voice signal to be recognized is input to the signal input 
terminal 1 and is fed to the first analyzer 3 where a parameter and power 
level of the input voice signal are calculated. The power level of the 
environmental noise and the power level of the inputted voice signal are 
input to the vowel deciding portion 4. At the vowel deciding portion 4, a 
vowel determination is made based on the above mentioned conditions (a) 
and (b) in the same manner as in the first embodiment. That is, a frame 
which satisfies the conditions (a) and (b) is determined to be a vowel. If 
it is determined that a frame is a vowel, the input signal is fed to the 
formant frequency detector 5. On the other hand, if it is determined that 
a frame is not a vowel, the input signal is fed to the matching portion 8. 
When a frame is determined to be a vowel, a formant frequency of the input 
voice signal is detected so as to compensate for the parameter of the 
input voice signal. The formant frequency is obtained in the same manner 
as in the first embodiment. Therefore, it is likewise possible to obtain 
the formant frequency from the LPC parameter. 
Subsequently, at the compensation value calculator 17, a compensation value 
of the LPC cepstrum is calculated by using a product of the formant 
frequency and a value obtained by differentiating the LPC cepstrum by the 
formant frequency in the same manner as in the first embodiment. The value 
obtained from the equation (4) is employed as the shift .DELTA.f of the 
formant frequency. When compensation is performed for only formants 
contained in a frequency range of 300-1,500 Hz and greatly changeable due 
to variations in voice signals, by setting the bandwidth bi of i-th 
formant and the sampling frequency fs at 150 Hz and 10 KHz, respectively, 
in the equation (1), the equation (1) is converted into the following 
equation (5) 
##EQU6## 
where 300 Hz&lt;fi&lt;1500 Hz. 
The compensation value calculated by the equation (5) is input to the 
parameter compensator 7 where the LPC cepstrum coefficient Cn is 
compensated for by the equation (3) in the same manner as in the first 
embodiment. The variation between the reference voice signal parameter and 
the input voice signal parameter is calculated in the matching portion 8 
in the case where the power level of the environmental noise is not more 
than the threshold value P1. Then, a recognition result is output from the 
output terminal 9. 
Meanwhile, the switches 11 to 14 are changed over in the same manner as in 
the first embodiment. 
As described above, in the second embodiment, the power level of the 
environmental noise is measured by the power measuring portion and the 
shift of the formant frequency due to voice signal variations is estimated 
by the shift estimator based on the equation (4) using the power level of 
the environmental noise. Meanwhile, the cepstral coefficient is calculated 
as the recognition parameter by the first analyzer and the formant 
frequency of the input voice signal is detected by the formant frequency 
detector. Furthermore, the compensation value is calculated by the 
compensation value calculator using the estimated shift of the formant 
frequency and the parameter of the input voice signal is compensated for 
by the parameter compensator by adding the compensation value to the 
parameter of the input voice signal in accordance with variations of the 
inputted voice signal. Therefore, it is possible to compensate for 
variations of the formant frequency due to utterances in a noisy 
environment and thus the recognition rate is improved. 
Meanwhile, by compensating for the recognition parameter itself, the 
contents of the compensation are accurately incorporated into the 
recognition parameter, thereby resulting in an improvement in the 
compensation efficiency. Furthermore, when the compensation value which is 
proper for the magnitude of the environmental noise is employed by 
estimating the shift of the formant frequency from the power level of the 
environmental noise, it is possible to further improve the compensation 
effects. 
FIG. 4 shows a word speech recognizer K3 according to a third embodiment of 
the present invention. The speech recognizer K3 includes a low-pass filter 
18 and a switch 19. Since other elements of the speech recognizer K3 are 
similar to those of the speech recognizer K1, a description thereof has 
been abbreviated for the sake of brevity. 
The operation of the speech recognizer K3 of the above described 
arrangement is described hereinbelow. Initially, at the time of the 
reference voice signal registration, noise contained in the environment 
surrounding the speech recognizer K3, immediately before the input of 
voice signals, are input to the signal input terminal 1 and the power 
level of the environmental noise is calculated by the power measuring 
portion 2. If the power level of the environmental noise exceeds the 
threshold value P1, the environment is regarded as being unsuitable for 
registration of the reference voice signals and the registration process 
is thus suspended. 
On the other hand, if the power level of the environmental noise is not 
more than the threshold value P1, the reference voice signal is input to 
the low-pass filter 18. After the cut-off frequency has passed through the 
2.5 KHz low-pass filter 18, the reference voice signal is fed to the first 
analyzer 3. At the first analyzer 3, the LPC cepstral coefficient having a 
predetermined order is calculated as the recognition parameter. Analysis 
is performed in the same manner as in the above first and second 
embodiments. The characteristic parameter in a frame in which the power 
exceeds a detection threshold value within a predetermined voice signal 
interval is stored in the buffer 10. The above described processing 
starting from the input of the reference voice signal is performed for all 
words to be recognized and the registration process is then complete. 
Subsequently, at the time of voice signal recognition the power level of 
environmental noise is measured by the power measuring portion 2 in the 
same manner as in the reference voice registration process and then, a 
voice signal to be recognized is input to the signal input terminal 1. The 
input voice signal is passed through the low-pass filter 18 in the same 
manner as in the reference voice registration process and is then fed to 
the first analyzer 3 where the LPC cepstral coefficient is calculated as 
the parameter of the input voice signal. 
If the power level of the environmental noise is not more than the 
threshold value P1, this parameter is input to the matching portion 8. In 
the matching portion 8, the variation between the reference voice signal 
parameter and the inputted voice signal parameter is calculated and a word 
exhibiting a minimum variation between the reference voice signal and the 
inputted voice signal is output as a recognition result from the output 
terminal 9. 
On the contrary, if the power level of the environmental noise exceeds the 
threshold value P1, the power level of the environmental noise is input to 
the vowel deciding portion 4 together with the power level of the input 
voice signal which is calculated in the analyzer 3 together with the 
parameter. At the vowel deciding portion 4, a vowel determination is made 
based on the above described conditions (a) and (b) in the same manner as 
in the first embodiment. If it is determined that a frame is a vowel, the 
parameter of the input voice signal is fed to the formant frequency 
detector 5. On the other hand, if it is determined that a frame is not a 
vowel, the parameter of the input voice signal is fed to the matching 
portion 8. 
When a frame is determined to be a vowel, a formant frequency of the input 
voice signal is detected by the formant frequency detector 5 so as to 
compensate for the parameter of the input voice signal. The formant 
frequency is obtained from the LPC parameter in the same manner as in the 
first embodiment. 
Subsequently, at the compensation value calculator 6, a compensation value 
of the LPC cepstrum is calculated using a product of the formant frequency 
and a value obtained by differentiating the LPC cepstrum by the formant 
frequency in the same manner as in the first embodiment. 
Under the same conditions as in the first embodiment, the compensation 
value of the equation (2) and the compensated LPC cepstral coefficient of 
the equation (3) are obtained in the same manner as in the first 
embodiment. 
The switch 19 actuates to change over to the power measuring portion 2 and 
the low-pass filter 18 when the power level of the environmental noise 
immediately before the input of the voice signal is measured and when the 
voice signal is input, respectively. The switches 12 to 14 are changed 
over in the same manner as in the first embodiment. 
As described above, in this embodiment, the high-band spectrum, in which 
voice patterns vary greatly and the formant power increases, is eliminated 
by the low-pass filter, while the linear prediction coefficient and the 
cepstral coefficient are calculated by the analyzer. Meanwhile, the low 
formant frequency is detected by the formant frequency detector and the 
compensation value is calculated in the compensation value calculator by 
using the equation (1). Furthermore, the parameter is compensated for by 
the compensation value in accordance with variations of the pattern of the 
input signal in the parameter compensator and the variation between the 
compensated parameter of the inputted voice signal and the parameter of 
the reference voice signal is calculated. Thus, since the low formant 
frequency is positively detected, changes of the formant frequency, which 
are primarily caused by the variations of the voice signals, can be 
reliably compensated for each input voice signal by using the detected 
formant frequency. Meanwhile, since the high-band spectrum in which voice 
patterns vary greatly has been eliminated by using the low-pass filter, it 
becomes possible to take into account the deviations of the recognition 
parameters caused by differences in voice signals. Consequently, it is 
possible to improve the recognition rate in a noisy environment. 
Lastly, FIG. 5 shows a word speech recognizer K4 according to a fourth 
embodiment of the present invention. The speech recognizer K4 includes a 
power measuring portion 20, second and third analyzers 21 and 23 and 
switches 24, 25 and 26. The second and third analyzers 21 and 23 are the 
same as the analyzers 21 and 23 of the known speech recognizer of FIG. 1, 
respectively. Since other elements of the speech recognizer K4 are similar 
to those of the speech recognizer K1, a description thereof has been 
abbreviated below for the sake of brevity. 
The operation of the speech recognizer K4 of the above described 
arrangement is described hereinbelow. Initially, at the time of 
registration of the reference voice signal, noise contained in the 
environment surrounding the speech recognizer K4, immediately before input 
of voice signals, is input to the signal input terminal 1 and the power 
level of the environmental noise is calculated by the power measuring 
portion 20. If the power level of the environmental noise exceeds the 
threshold value P1, the environment is regarded as being unsuitable for 
registration of the reference voice signal and thus, the registration 
process is suspended. 
On the other hand, if the power level of the environmental noise is not 
more than the threshold value, a reference voice signal is input to the 
signal input terminal 1 and is fed to the second analyzer 21 where an LPC 
cepstral coefficient is calculated as a recognition parameter. At the 
second analyzer 21, the input signal is passed through a filter F1 
expressed by the following equation (6): 
EQU F1(Z)=1-0.9375.times.Z.sup.-1 (6) 
where the character Z denotes a Z-function. 
After the high-band spectrum of the input signal has been emphasized by the 
filter F1, the input signal is analyzed. When the power level of the 
environmental noise exceeds a detection threshold value within a 
predetermined voice signal interval, the corresponding characteristic 
parameter is regarded as the reference voice signal parameter to be stored 
in the buffer 10. The above described processing starting from the input 
of the reference voice signal is performed for all words to be recognized 
and the registration process is then complete. 
Subsequently, at the time of recognition of a voice signal, the power level 
of environmental noise is measured in the same manner as in the reference 
voice registration process and then, a voice signal to be recognized in 
input to the signal input terminal 1. If the power level of the 
environmental noise is not more than the threshold value P1, the input 
voice signal is passed through the filter F1 in the same manner as in the 
reference voice registration process and then, a characteristic parameter 
of the input voice signal is calculated in the second analyzer 21 so as to 
be input to the matching portion 8. In the matching portion 8, the 
variation between the reference voice signal parameter and the input voice 
signal parameter is calculated and a word exhibiting a minimum variation 
between the reference voice signal parameter and the input voice signal 
parameter is output as a recognition result from the output terminal 9. 
On the other hand, if the power level of the environmental noise exceeds 
the threshold value P1, power level of the voice signal is calculated for 
each frame by the power measuring portion 20. Then, the power level of the 
environmental noise and the power level of the voice signal are input to 
the vowel deciding portion 22. At the vowel deciding portion, a vowel 
determination is made based on the above described conditions (a) and (b) 
in the same manner as in the first embodiment. Hence, it is determined 
that a frame satisfying the conditions (a) and (b) is a vowel. If it is 
determined that a frame is a vowel, the input voice signal is fed to the 
third analyzer 23. On the other hand, if it is determined that a frame is 
not a vowel, the input voice signal is fed to the second analyzer 21. 
When a frame is determined to be a vowel, a high-frequency band of the 
frame is emphasized by a filter F2 expressed by the following equation 
(7). 
EQU F2(Z)=1-0.6375.times.Z.sup.-1 (7) 
Emphasis of the high-frequency band of the frame by the filter F2 is less 
than that of the filter F1 and the tilt of the equation (7) is milder than 
that of the equation (6). When the environmental noise becomes large, the 
utterance state of a speaker changes such that the high-band spectrum of 
the voice signal becomes intense. Therefore, the tilt of the filter for 
emphasizing the high-band spectrum in the noisy environment is required to 
be milder than that in the less noisy environment. After the input voice 
signal has passed through the filter F2, the characteristic parameter of 
the input voice signal is obtained in the same manner as in the reference 
voice signal registration process. 
Subsequently, the formant frequency of the input voice signal is detected 
by the formant frequency detector 5. The formant frequency is obtained 
from the LPC parameter in the same manner as in the first embodiment. 
Thereafter, a compensation value of the LPC cepstrum is calculated by the 
compensation value calculator 6 in the same manner as in the first 
embodiment. 
Under the same conditions of the first embodiments the compensation value 
of the equation (2) and the compensated LPC cepstrum of the equation (3) 
are obtained in the same manner as in the first embodiment. 
The switch 24 actuates to change over to the vowel deciding portion 4 and 
to the second analyzer 21 when the power level of the environmental noise 
exceeds the threshold value P1 and when the same is not more than the 
threshold value P1, respectively. The switch 25 actuates to change over to 
the buffer 10 and the matching portion 8 at the time of the registration 
process and at the time of the recognition process, respectively. The 
switch 26 actuates to change over to the third analyzer 23 and the second 
analyzer 22 when a frame is determined to be a vowel and when a frame is 
determined not to be a vowel, respectively. 
As described above, in this embodiment, the linear coefficient of the 
filter for emphasizing the high-band spectrum is changed only for a voice 
signal which varies greatly in a noisy environment having a small 
signal-to-noise ratio, in the third analyzer 23, such that emphasis of the 
high-band spectrum of the voice is lessened. Then, the linear prediction 
coefficient and the cepstrum parameter are calculated and the low formant 
frequency is detected by the formant frequency detector. Meanwhile, the 
compensation value is calculated in the compensation value calculator by 
using the formant frequency and the transformation nucleus of each 
parameter. Furthermore, the parameter of the input signal is compensated 
for in accordance with changes in the formant frequency o the input signal 
by the parameter compensator and the variation between the compensated 
parameter of the input voice signal and the parameter of the reference 
voice signal is calculated by the matching portion. Therefore, the peak of 
the high formant frequency is restricted to a low level. As a result, the 
low formant frequency can be accurately detected. By using the detected 
formant frequency, changes of the formant frequency, which primarily cause 
variations in voice signals, can be accurately compensate for each input 
voice signal. 
Meanwhile since emphasis of the high-band spectrum is lessened, the power 
level of the high-band spectrum caused by variations in voice signals is 
restricted, so that it is possible to take into account deviations of the 
recognition parameter caused by differences in voice signals. 
Consequently, the recognition rate in a noisy environment can be improved. 
Although the present invention has been fully described by way of example 
with reference to the accompanying drawings, it is to be noted here that 
various changes and modifications will be apparent to those skilled in the 
art. Therefore, unless otherwise such changes and modifications depart 
from the scope of the present invention, they should be construed as being 
included therein.