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Claims  |
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What is claimed is:
1. A wideband speech signal reconstruction method comprising:
a first step wherein an input narrowband speech signal is
spectrum-analyzed;
a second step wherein the spectrum-analyzed results obtained in said first
step are vector-quantized using a narrowband speech signal codebook;
a third step wherein the quantized values obtained in said second step are
decoded to codevectors using a wideband speech signal codebook; and
a fourth step wherein said codevectors obtained in said third step are
spectrum-synthesized to obtain a wideband speech signal.
2. The method of claim 1 further comprising:
a fifth step wherein said input narrowband speech signal is up-sampled to
compute sample values;
a sixth step wherein frequency components outside the band of said input
narrowband speech signal are extracted from said wideband speech signal
obtained in said fourth step; and
a seventh step wherein said out-of-band frequency components obtained in
said sixth step are added to said sample values obtained in said fifth
step to obtain a wideband speech signal.
3. The method of claim 1 or 2 wherein said narrowband speech signal
codebook is composed of codevectors obtained by: spectrum-analyzing a
training wideband speech signal; vector-quantizing the results of said
spectrum analysis through use of a wideband speech signal codebook;
extracting a narrowband speech signal from said training wideband speech
signal; spectrum-analyzing said extracted narrowband speech signal;
sequentially associating the results of said spectrum analysis and the
results of said vector quantization with each other to form clusters; and
averaging the results of said spectrum analysis of said extracted
narrowband speech signal for each cluster.
4. A wideband speech signal reconstruction method comprising:
a first step wherein an input narrowband speech signal is
spectrum-analyzed;
a second step wherein the spectrum-analyzed results obtained in said first
step are vector-quantized using a narrowband speech signal codebook; and
a third step wherein the quantized values obtained by said vector
quantization in said second step are reconstructed to obtain a wideband
speech signal through use of a representative waveform codebook.
5. The method of claim 4 further comprising:
a fourth step wherein said input narrowband speech signal is up-sampled to
compute sample values;
a fifth step wherein frequency components outside the band of said input
narrowband speech signal are extracted from said wideband speech signal
obtained in said third step; and
a sixth step wherein said out-of-band frequency components obtained in said
filth step and said sample values obtained in said fourth step are added
together to obtain a wideband speech signal.
6. The method of claim 4 or 5 wherein said representative waveform codebook
is composed of representative waveform segments obtained by a procedure
wherein a training wideband speech signal is spectrum-analyzed, the
spectrum-analyzed results are matched with a wideband speech signal
codebook and, for each code of said codebook, the waveform of said
training wideband speech signal corresponding to the spectrum-analyzed
result closest to the codevector of the code is selected by one pitch for
voiced speech and by one to two analysis window lengths for unvoiced
speech, said selected waveform being used as a representative segment of
the said code.
7. A wideband speech signal reconstruction method comprising:
a first step wherein an input narrowband speech signal is
spectrum-analyzed;
a second step wherein the spectrum-analyzed results in said first step are
vector-quantized using a narrowband speech signal codebook;
a third step wherein the quantized values obtained in said second step are
decoded to codevectors, using a wideband speech signal codebook;
a fourth step wherein the codevectors decoded in said third step are
spectrum-synthesized to a wideband speech signal;
a fifth step wherein frequency components lower than the band of said input
narrowband speech signal are extracted from said wideband speech signal
obtained in said fourth step;
a sixth step wherein said quantized values obtained in said second step are
decoded to obtain a high-frequency speech signal, using a representative
waveform codebook of a high-frequency speech signal higher than the band
of said input narrowband speech signal;
a seventh step wherein said input narrowband speech signal is up-sampled to
compute sample values; and
an eighth step wherein said lower-frequency components obtained in said
fifth step, said high-frequency speech signal obtained in said sixth step
and said sample values computed in said seventh step are added together to
obtain a wideband speech signal.
8. The method of claim 4, 5, or 7 wherein, in the reconstruction of said
quantized values to a speech signal through use of said representative
waveform codebook, waveform segments of said representative waveform
codebook corresponding to said quantized values are overlapped
pitch-synchronously for voiced speech and waveforms of a length
corresponding to an analysis window shift width are randomly selected for
unvoiced speech.
9. The method of claim 7 further comprising a ninth step wherein the power
of said lower-frequency components extracted in said fifth step is
increased to a level corresponding to the power of said narrowband signal
before being supplied to said eighth step, and a tenth step wherein the
power of said high-frequency speech signal obtained in said sixth step is
adjusted in accordance with the power of said input narrowband speech
signal.
10. The method of claim 9 wherein said ninth step also decodes said
quantized values obtained in said second step to codevectors, using a
narrowband representative waveform codebook, spectrum synthesizes said
decoded codevectors to obtain a narrowband speech signal, obtains the
ratio between the power of said narrowband speech signal and the power of
said lower-frequency components obtained in said fifth step, and
multiplies the power of said high-frequency speech signal obtained in said
sixth step by said ratio.
11. A wideband speech signal reconstructing apparatus comprising:
means for spectrum-analyzing an input narrowband speech signal;
means for vector-quantizing the results, obtained by said
spectrum-analyzing means, by use of a narrowband speech signal codebook;
means for decoding the vector-quantized values, obtained by said
vector-quantizing means, to codevectors through use of a wideband speech
signal codebook; and
means for spectrum-synthesizing said codevectors, obtained by said decoding
means, to obtain a synthesized wideband speech signal.
12. The apparatus of claim 11 further comprising:
means for up-sampling said input narrowband speech signal to compute sample
values;
filter means for extracting out-of-band components outside the band of said
input narrowband speech signal from said synthesized wideband speech
signal; and
means for adding said out-of-band components to said sample values to
obtain a wideband speech signal.
13. A wideband speech signal reconstructing apparatus comprising:
means for spectrum-analyzing an input narrowband speech signal;
means for vector-quantizing the results, obtained by said
spectrum-analyzing means, by use of a narrowband speech signal codebook;
and
speech synthesizing means utilizing a representative waveform codebook for
reconstructing the vector-quantized values, obtained by said
vector-quantizing means, to obtain a synthesized wideband speech signal.
14. The apparatus of claim 13 further comprising:
means for up-sampling said input narrowband speech signal to compute sample
values;
filter means for extracting out-of-band components outside the band of said
input narrowband speech signal from said synthesized wideband speech
signal obtained by said speech synthesizing means; and
means for adding together said out-of-band components and said sample
values to obtain a wideband speech signal.
15. A wideband speech signal reconstructing apparatus comprising:
means for spectrum-analyzing an input narrowband speech signal;
means for vector-quantizing the results, obtained by said
spectrum-analyzing means, by use of a narrowband speech signal codebook;
means for decoding the quantized values, obtained by said vector-quantizing
means, to codevectors through use of a wideband speech signal codebook;
first speech synthesizing means for spectrum-synthesizing said codevectors,
obtained by said decoding means, to obtain a wideband speech signal;
filter means for extracting, from said wideband speech signal obtained by
said first speech synthesizing means, frequency components lower than the
band of said input narrowband speech signal;
second speech synthesizing means for decoding said quantized values,
obtained by said vector-quantizing means, to obtain a high-frequency
speech signal through use of a representative waveform codebook of a
high-frequency speech signal higher than the band of said input narrowband
speech signal;
means for up-sampling said input narrowband speech signal to compute sample
values; and
means for adding together said lower-frequency components obtained by said
filter means, said high-frequency speech signal obtained by said second
speech synthesizing means, and said sample values obtained by said
up-sampling means, to obtain a wideband speech signal.
16. The apparatus of claim 15 further comprising:
first power adjusting means for increasing the power of said
lower-frequency components at a fixed ratio and supplying the increased
power lower-frequency components to said adding means; and
second power adjusting means for adjusting the power of said high-frequency
speech signal in accordance with the power of said input narrowband speech
signal and supplying the power adjusted high-frequency speech signal to
said adding means. |
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Claims |
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Description |
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BACKGROUND OF THE INVENTION
The present invention relates to a method for reconstructing a wideband
speech signal from an input narrowband speech signal and, more
particularly, to a method and an apparatus whereby a narrowband speech
signal like present telephone speech or output signal from an AM radio can
be graded up to a wideband speech signal like an output signal from an
audio set or FM radio.
Telephone speech will be described as an example of the narrowband speech
signal. The spectrum band of a signal that the existing telephone system
can transmit is in the range of from about 300 Hz to 3.4 kHz. Conventional
speech coding techniques are intended to keep the quality of speech in
this telephone band and minimize the number of parameters that must be
transmitted. Thus, it is possible with the conventional speech coding
techniques to reconstruct band-limited input speech but impossible to
obtain higher quality speech.
In Japanese Patent Application Laid-Open No. 254223/91 entitled "Analog
Data Transmission System" there is proposed a system which transmits
analog data after removing its high-frequency component at the
transmitting side and reconstructs the high-frequency component at the
receiving side through use of a neural network pre-trained in accordance
with characteristics of the data. While this system transmits a narrowband
signal of only the low-frequency band over the transmission line with a
view to efficiently utilizing its transmission band, it can be said that
at the receiving side the high-frequency component is reconstructed from
the narrowband signal of the low-frequency component to recover the
original wideband signal. The speech signal includes, however, spectrum
information, pitch information and phase information, and it is unknown
for which information the neural network has been trained; hence, there is
no guarantee of correct reconstruction of the high-frequency component
with respect to the data for which the network has not been trained. To
train the neural network for all of such pieces of information, it is
necessary to significantly increase the number of intermediate or hidden
layers and the number of units of each layer--this makes it very
difficult, in practice, to train the neural network.
With the recent progress of acoustics technology and development of digital
processing, the quality of sound in everyday life has been improved and it
has come to be said that the quality of speech in the telephone band at
present is not satisfactory to many people. One possible solution to this
problem is to replace the existing telephone system with a new one that
permits the transmission of wideband signals, but this consumes
considerable time as well as involves enormous construction costs.
It is therefore a primary object of the present invention to provide a
wideband speech signal reconstruction method and apparatus which permit
reconstruction of a wideband speech signal from an input narrowband speech
signal transmitted with a view to efficient utilization of the existing
telephone system, for instance, and which allow the use of a wideband
speech signal even in a situation of the combined use of a wideband
telephone system capable of transmitting a wideband signal and the
existing narrowband telephone system.
SUMMARY OF THE INVENTION
According to an aspect of the present invention: in a first step an input
narrowband speech signal is analyzed to obtain spectrum; in a second step
the spectrum results are vector-quantized using a prepared narrowband
speech signal codebook; in a third step the vector-quantized values or
codes are decoded using a prepared wideband speech signal codebook; and in
a fourth step using the decoded values or codes a wideband speech signal
is synthesized. The narrowband speech signal codebook is generated using
narrowband speech signals and the wideband speech signal codebook is
similarly generated using wideband speech signals; where codevectors of
one codebook have one-to-one correspondence to codevectors of the other
codebook.
In another aspect of the present invention: in a fifth step the input
narrow speech signal is up-sampled; in a sixth step frequency components
outside the frequency band of the input narrowband speech signal are
extracted from the wideband speech signal obtained in the fourth step; and
in a seventh step the extracted out-of-band components and the up-sampled
signals obtained in the fifth step are added together to obtain a wideband
speech signal.
The narrowband speech signal codebook and the wideband speech signal
codebook are associated with each other in such a manner as described
below. A training wideband speech signal is down-sampled and then filtered
to obtain a training narrowband speech signal. These training wideband and
narrowband speech signals are respectively analyzed to obtain spectrum and
the spectrum of the wideband speech signal are vector-quantized into code
numbers, using the aforementioned wideband speech signal codebook. The
quantized results, i.e. the code numbers, and the spectrum of the
narrowband speech signal are associated with each other for each analysis
frame. The spectrums of the narrowband speech signal are classified into
clusters, that is, the spectrums of the narrowband speech signal are
collected for each quantized code, and then the collected spectrums are
averaged for each code or cluster to obtain codevectors, which are used to
form the narrowband speech signal codebook.
According to another aspect of the present invention: in a first step an
input narrowband speech signal is analyzed to obtain spectrum; in a second
step the spectrum are vector-quantized using a prepared narrowband speech
signal codebook; and in a third step the vector-quantized values or codes
are reconstructed into a wideband speech signal, using a prepared
representative waveform codebook.
In another aspect of the present invention: in a fourth step the input
narrowband speech signal is up-sampled; in a fifth step frequency
components outside the input narrowband speech signal are extracted from
the wideband speech signal obtained in the third step; and in a sixth step
the thus extracted out-of-band components are added to the up-sampled
signals to provide a wideband speech signal.
The above-mentioned representative waveform codebook is produced in such a
manner as described below. A training wideband speech signal is analyzed
to obtain spectrum; and the spectrum are matched with a prepared wideband
speech signal codebook. For each codevector of the codebook, the waveform
of the training wideband speech signal, where spectrum is the closest to
the spectrum of the codevector is extracted by one pitch in the case of
voiced speech and by one or two analysis window lengths in the case of
unvoiced speech, and the thus extracted waveform is used as a
representative waveform segment of the codevector.
According to still another aspect of the present invention: in a first step
an input narrowband speech signal is analyzed to obtain spectrum; in a
second step the spectrum are vector-quantized into code numbers, using a
prepared narrowband speech signal codebook; in a third step the code
numbers are decoded to codevectors using a prepared wideband speech signal
codebook and using the thus decoded codevectors, wideband speech signal is
synthesized; in a fourth step frequency components lower than the input
narrowband speech signal are extracted from the synthesized wideband
speech signal to reconstruct a low-frequency signal; in a fifth step a
high-frequency signal is reconstructed, for each code number obtained in
the second step, using a prepared representative waveform codebook which
contains frequency components higher than the narrowband speech signal; in
a sixth step the input narrowband speech signal is up-sampled; and in a
seventh step the up-sampled signal, the reconstructed low-frequency signal
and the reconstructed high-frequency signal are added together to obtain a
wideband speech signal.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a diagram showing the procedure for generating a wideband speech
signal codebook;
FIG. 2 is a diagram showing the procedure for generating a narrowband
speech signal codebook;
FIG. 3 is a diagram for explaining the operations involved in the procedure
of FIG. 2;
FIG. 4 is a block diagram illustrating an embodiment of the present
invention;
FIG. 5 is a diagram showing the procedure for generating a representative
waveform codebook;
FIG. 6 is a diagram for explaining the operations involved in the procedure
of FIG. 5;
FIG. 7 is a block diagram illustrating another embodiment of the present
invention;
FIG. 8 is a block diagram showing the configuration of a part for
reconstructing frequency components lower than an input narrowband speech
signal according to the present invention;
FIG. 9 is a diagram showing the procedures for producing a narrowband
representative waveform codebook and a highband representative waveform
codebook;
FIG. 10 is a block diagram illustrating the configuration of a part for
reconstructing frequency components higher than the input narrowband
speech signal according to the present invention; and
FIGS. 11A and 11B are graphs showing the relationships between distortion
by vector quantization, distortion by reconstruction according to the
present invention and the codebook size.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
A description will be given first, with reference to FIG. 1, of the
procedure for creating a wideband speech signal codebook that is used in
the present invention. This procedure is well-known in the art. To
efficiently express features of a training wideband speech signal,
parameters that appropriately express features of the wideband speech
signal are classified into clusters, which are used to provide the
codebook. Parameters that can be used to characterize a speech signal are
speech spectrum envelopes by linear predictive coding (LPC) and an FFT
cepstrum analysis method and parameters by a PSE speech analysis-synthesis
method and a speech expression method using sine waves. This example will
be described in connection with the case of using the speech spectrum
envelopes by LPC as such feature parameters. The codebook generating
procedure starts with step 101 wherein an input training wideband speech
signal of an 8 kHz band, for instance, is converted by an
analog-to-digital (A/D) converter to a digital signal. Then, in step 102
the digital signal is subjected to an LPC analysis to obtain a parameter
such as spectrum data (an auto-correlation function and an LPC cepstrum
coefficients). These parameters are collected from a sufficiently large
number of words, say, 200 words. Then, in step 103 the parameters thus
collected are classified into clusters. This clustering is performed
through use of an LBG algorithm, and the acoustic distance measure that is
utilized in the clustering is a Euclidean distance of an LPC cepstrum as
shown below by Eq. (1).
##EQU1##
where C and C' are LPC cepstrum coefficients obtained by LPC analysis of
different speech signals and p is the order of the LPC cepstrum
coefficient.
Incidentally, the above-mentioned LBG algorithm is described in detail in
Linde, Buzo, Gray, "An Algorithm for Vector Quantization Design," IEEE
COM-23 (1980-01).
The above equation (1) is used to obtain a wideband speech signal codebook
104.
According to a first aspect of the present invention, a narrowband speech
signal codebook, which is associated with the wideband speech signal
codebook 104, is utilized. With reference to FIG. 2 an example of
generating the narrowband speech signal codebook will be described while
maintaining its correspondence to the wideband speech signal codebook 104.
This processing is intended to pre-obtain signal features that are absent
in an input narrowband speech signal but ought to present in a wideband
speech signal that will ultimately be output. The process begins with
down-sampling of a training wideband speech signal in step 200, followed
by step 201 wherein the resulting sample values are used to extract, from
the training wideband speech signal, a signal of the same band as that of
the input narrowband speech signal. The down-sampling is described in L.
Rabiner, R. Schafer, "Digital Processing of Speech Signal," Chapter 2,
Prentice-Hall, Inc., 1978, for example. This embodiment will be described
on the assumption that the training wideband speech signal is a speech
signal of the 8 kHz band and the narrowband speech signal is a speech
signal of the telephone band (300 Hz to 3.4 kHz). Hence, in step 201 a
narrowband speech signal is produced by passing the training wideband
speech signal through a high-pass filter that removes frequencies below
300 Hz and a low-pass filter that removes frequencies above 3.4 kHz. On
the other hand, the input training wideband speech signal is subjected to
LPC analysis in step 202, after which in step 203 the analyzed values are
vector-quantized using the wideband speech signal codebook 104 that was
obtained following the procedure described above in respect of FIG. 1.
Incidentally, since the narrowband speech signal is one that has been
derived from the wideband speech signal, the temporal correspondence
between these signals can be made a one-to-one correspondence between
their LPC analysis frame numbers. Hence, the narrowband speech signal
corresponding to the training wideband speech signal that was
vector-quantized in step 203 is obtained for each frame in step 201, and
the thus obtained narrowband LPC analyzed in step 205, after which in step
206 the analyzed values are classified and stored for each of codevector
number obtained by the vector quantization in step 203. That is, let it be
assumed that a wideband speech signal, shown in FIG. 3, Row A, is
quantized in step 203 for respective frames Nos. 1, 2, 3, . . . shown in
FIG. 3, Row B to obtain codes C.sub.5, C.sub.11, C.sub.9, . . . as
depicted in FIG. 3, Row C and that vectors V.sub.5, V.sub.11, V.sub.9, . .
. , obtained by the LPC analysis of the narrowband speech signal derived
from the wideband speech signal shown in FIG. 3, Row A are obtained in
correspondence to the frames Nos. 1, 2, 3, . . . as depicted in FIG. 3,
Row D. Then, LPC-analyzed vectors, for example, V.sub.5, V.sub.5 ',
V.sub.5 ", . . . of respective narrowband speech signals, obtained for the
same code No. C.sub.5, are collected and stored; similarly, vectors
V.sub.11, V.sub.11 ', V.sub.11 ", . . . for the code No. C.sub.11 are
collected and stored. In this way, the LPC-analyzed vectors of the
respective narrowband speech signal are collected and stored for all of
the code numbers of the wideband speech signal codebook 104. The
processing from step 201 to step 206 is performed for all training
wideband speech signals corresponding to 200 words, for instance. In step
207 the LPC-analyzed values stored or retained in step 206 through the
above-described processing are averaged for each cluster (for each code
number) and then a narrowband speech signal codebook 208 is produced using
the averaged values as codevectors corresponding to the respective code
numbers.
Next, a description will be given, with reference to FIG. 4, of a first
embodiment of the present invention which reconstructs a wideband speech
signal from an input narrowband speech signal through utilization of the
wideband speech signal codebook 104 and the narrowband speech signal
codebook 208 associated with each other as described above. The input
narrowband speech signal is LPC-analyzed by an LPC analyzer 301 and the
obtained parameters are subjected to fuzzy vector quantization by
quantizer 302 using the narrowband speech signal codebook 208. The fuzzy
vector quantization is described in H. Tseng, M. Sabin, E. Lee, "Fuzzy
Vector Quantization Applied to Hidden Markov Modeling," ICASSP'87 15.5
Apr. 1987. To reduce the computational quantity involved, the processing
by the quantizer 302 may be ordinary vector quantization. This embodiment
will be described to employ fuzzy vector quantization with a view to
synthesizing smoother speech signals. The fuzzy vector quantization is a
scheme that approximates an input vector with k codevectors close thereto
as shown below by Eq. (2) and the output is a fuzzy membership function
u.sub.i.
##EQU2##
where d.sub.i is the Euclidean distance between the input vector and that
one V.sub.i of the k codevectors in the codebook 208 which is close to the
input vector, and m is a constant that determines the degree of fuzziness.
Then, fuzzy-vector-quantized codes from the quantizer 302 by decoded 304
using the wideband speech signal codebook 104 as shown below by Eq. (3).
##EQU3##
where X' is the decoded vector.
The decoded output X' is LPC-synthesized by a speech synthesizer 306 to
obtain a wideband speech signal. That is, an excitation signal, which
depends on the pitch obtained from the LPC-analyzed values by the LPC
analyzer 301, is used to drive a synthesis filter and its filter
coefficient is controlled in accordance with the decoded output X'. Speech
power is set to the values obtained by the LPC analyzer 301. This
synthetic speech signal may be output as a reconstructed wideband speech
signal.
The wideband speech signal thus produced is one that contains signal
components outside the frequency band of the input narrowband speech
signal and also contains, inside the band of the input narrowband speech
signal, signal components different therefrom, and these signal components
distort the input narrowband speech signal. In view of this, the
processing described below is performed so that the signals primarily
present in the input narrowband speech signal are used intact. That is,
the wideband speech signal synthesized by the LPC analyzer 306 is applied
to a band-pass filter 307 to extract components outside the band of the
input narrowband speech signal, that is, frequency components below 300 Hz
and those above 3.4 kHz. On the other hand, the input narrowband speech
signal is up-sampled by an up-sampler 308 to the 8 kHz band. The output
from the up-sampler 308 and the extracted components from the band-pass
filter 307 are added together by an adder 309 to thereby obtain a
reconstructed wideband speech signal. Incidentally, the up-sampling is
carried out by applying the input narrowband speech signal to an allpass
filter after inserting a "zero" sample between adjacent sample points and
then by sampling the filter output at a twofold speed to double the
frequency band of the speech signal. This up-sampling is described in L.
Rabiner, R. Schafer, "Digital Processing of Speech Signal," Chapter 2,
Prentice-Hall, Inc. 1978, for instance.
The spectrum analysis in step 102 in FIG. 1, steps 202 and 205 in FIG. 2
and in the LPC analyzer 301 in FIG. 4 is to obtain parameters of the same
kind by the same analysis method. The training wideband speech signal that
is used to generate the narrowband speech signal in FIG. 2 need not always
be the wideband speech signal used in the creation of the wideband speech
signal codebook 104.
Next, a description will be given, with reference to FIG. 5, of the
procedure for producing a representative waveform codebook that is used
according to a second aspect of the present invention. The training
wideband speech signal used to create the wideband speech signal codebook
104 shown in FIG. 1, or a different training wideband speech signal of
about the same frequency band as that of the above is converted by an
analog-to-digital (A/D) converter in step 101. In step 102 the digital
signal is subjected to LPC analysis to obtain parameters such as spectrum
data or information (an auto-correlation function and an LPC cepstrum
coefficient). The parameters are assumed to be identical with those used
in the production of the codebook 104 in FIG. 1; hence, the parameters
obtained in step 103 in FIG. 1 may also be used. These parameters are
collected from a sufficiently large number of words, for example, 200
words, and in step 211 the waveform of the frame closest to each
codevector is selected by reference to the wideband speech signal codebook
104 produced in FIG. 1. Let it be assumed, for instance, that in the case
where the input training wideband speech signal has such a waveform as
shown in FIG. 6, Row A and the frames in the LPC analysis are numbered as
shown in FIG. 6, Row B, the codevector that is the closest to the LPC
analysis result, obtained in step 102, is retrieved from the wideband
speech signal codebook 104 for each frame and, as a result, codevectors
V.sub.7, V.sub.9, V.sub.1, . . . are determined for the frames Nos. 1, 2,
3, . . . as depicted in FIG. 6, Row C. After completion of the
determination of the codevectors for all training wideband speech signals,
the same codevector, for example, V.sub.7, appears in the frames Nos. 1,
5, 8, . . . in this example, and if that one of these frames which is the
closest to the LPC analysis result of the current training wideband speech
signal is the frame No. 5, for example, the waveform of the training
wideband speech signal in the frame No. 5 is used as a representative
waveform segment for the codevector V.sub.7. Similarly, representative
waveform segments for the other remaining codevectors are selected. In
practice, the representative waveform segments are selected in step 211 as
follows: The waveform of the training wideband speech signal that has a
one analysis window length (in the LPC analysis) centering about each
frame of the signal is extracted by one pitch in the case of voiced speech
and by one or two analysis window lengths in the case of unvoiced speech,
and the extracted waveform is used as the representative waveform segment
for the code number concerned. In this way, a representative codebook 212
is produced which has stored therein the representative waveform segments
for the respective code numbers of the codebook 104. The frame length is
equal to the window shift width in the LPC analysis.
Turning next to FIG. 7, a description will be given of the procedure for
reconstructing a wideband speech signal from a narrowband speech signal
according to the second aspect of the present invention. An input
narrowband speech signal of a band ranging from 300 Hz to 3.4 kHz, for
instance, is LPC analyzed by an LPC analyzer 401 to obtain the same
spectrum parameters as those used in FIG. 1, and the spectrum parameters
are vector-quantized by a vector quantizer 402. This vector quantization
utilizes the narrowband speech codebook 208 produced by the method
described previously in respect of FIG. 2. Next, a wideband speech signal
is reconstructed in a waveform synthesizer 404 as follows: First,
representative waveform segments corresponding to respective code numbers
obtained by the quantizer 402 are extracted by a waveform extractor 404A
from the representative waveform codebook 212 produced in FIG. 5. Voiced
speech is synthesized by pitch-synchronous overlapping of the extracted
representative waveform segments and unvoiced speech is synthesized by
randomly using waveforms of a length corresponding to the window shift
width (in the LPC analysis). By this, a wideband speech signal of an 8 kHz
band, for instance, is reconstructed. This wideband speech signal can be
output as a reconstructed signal. The synthesis by pitch-synchronous
overlapping is described in E. Moulines, F. Charpentier,
"Pitch-synchronous Waveform Processing Techniques for Text-to-Speech
Synthesis using Diphones," Speech Communication, Vol. 9, pp. 453-567, Dec.
1990, for instance.
The wideband speech signal obtained by the processing described above
contains not only signal components outside the band of the input
narrowband speech signal but also signal components inside the band of the
input narrowband speech signal; the signal components inside the band of
the input signal distort the input narrowband speech signal. A solution to
this problem is to perform the processing described below. The wideband
speech signal provided by the waveform synthesizer 404 is applied to a
band-pass filter 405 to extract frequency components below 300 Hz and
those above 3.4 kHz; namely, out-of-band signals outside the band of the
input narrowband speech signal are extracted. On the other hand, the input
narrowband speech signal is up-sampled by an up-sampler 406 to the 8 kHz
band, and the sample values and the out-of-band signals from the band-pass
filter 405 are added together by an adder 407 to obtain | | |
