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TECHNICAL FIELD OF THE INVENTION
The present invention relates in general to speech processing methods and
apparatus, and more particularly relates to methods and apparatus for
encoding and decoding speech information for digital transmission at a
very low rate, without substantially degrading the fidelity or
intelligibility of the information.
BACKGROUND OF THE INVENTION
The transmission of information by digital techniques is becoming the
preferred mode of communicating voice and data information. High speed
computers and processors, and associated modems and related transmission
equipment, are well adapted for transmitting information at high data
rates. Telecommunications and other types of systems are well adapted for
transmitting voice information at data rates upwardly of 64 kilobits per
second. By utilizing multiplexing techniques, transmission mediums are
able to transmit information at even higher data rates.
While the foregoing represents one end of an information communication
spectrum, there is also a need for providing communications at low or very
low data rates. Underwater and low speed magnetic transmission mediums
represent situations in which communications at low data rate are needed.
The problems attendant with low data rate transmissions is that it is
difficult to fully characterize an analog voice signal, or the like, with
a minimum amount of data sufficient to accommodate the very low
transmission data rate. For example, in order to fully characterize speech
signals by pulse amplitude modulation techniques, a sampling rate of about
8 kHz is necessary. Obviously, digital signals corresponding to each pulse
amplitude modulated sample cannot be transmitted at very low transmission
bit rates, i.e., 200-1200 bits per second. While some of the digital
signals could be excluded from transmission to reduce the bit rate,
information concerning the speech signals would be lost, thereby degrading
the intelligibility of such signals at the receiver.
Various approaches have been taken to compress speech information for
transmission at a very low data rate without compromising the quality or
intelligibility of the speech information. To do this, the dynamic
characteristics of speech signals are exploited in order to encode and
transmit only those characteristics of the speech signals which are
essential in maintaining the intelligibility thereof when transmitted at
very low data rates. Quantization of continuous-amplitude signals into a
set of discrete amplitudes is one technique for compressing speech signals
for very low data rate transmissions. When each of a set of signal value
parameters are quantized, the result is known as scalar quantization. When
a set of parameters is quantized jointly as a single vector, the process
is known as vector quantization. Scalar and vector quantization techniques
have been utilized to transmit speech information at low data rates, while
maintaining acceptable speech intelligibility and quality. Such techniques
are disclosed in the technical article "Vector Quantization In Speech
Coding", Proceedings of the IEEE, Vol. 73, No. 11, Nov., 1985.
Matrix quantization of speech signals is also well-known in the art for
deriving essential characteristics of speech information. Matrix
quantization techniques require a large number of matrices to characterize
the speech information, thereby being processor and storage intensive, and
not well adapted for low data rate transmission. A significant degradation
of the intelligibility of the speech information results when employing
matrix quantization and low data rate transmissions.
When vector quantizing a signal for transmission, a vector "X" is mapped
onto another real-valued, discrete-amplitude, N-dimensional vector "Y".
Typically, the vector "Y" takes on one definite set of values referred to
as a codebook. The vectors comprising the codebook are utilized at the
transmitting and receiving ends of the transmission system. Hence, when a
number of parameters characteristic of the speech information are mapped
into one of the codebook vectors, only the codebook vectors need to be
transmitted to thereby reduce the bit rate of the transmission system. The
reverse operation occurs at the receiver end, whereupon the vector of the
codebook is mapped back into the appropriate parameters for decoding and
resynthesizing into an audio signal. While matrix quantization offers one
technique for compressing speech information, the intelligibility suffers,
in that one generally cannot discriminate between speakers.
From the foregoing, it can be seen that a need exists for a speech
compression technique compatible with data rates on the order of 400 bits
per second, without compromising speech quality or intelligibility. An
associated need exists for a speech compression technique which is
cost-effective, relatively uncomplicated and can be carried out utilizing
present day technology.
SUMMARY OF THE INVENTION
In accordance with the present invention, the disclosed speech compression
method and apparatus substantially reduces or eliminates the disadvantages
and shortcomings associated with the prior art techniques. According to
the invention, the speech signals are digitized and framed, and a number
of frames are encoded without regard to phonemic boundaries to provide a
fixed data rate encoding system. The technical advantage thereby presented
is that the system is more immune to transmission noise, and such a
technique is well adapted for self-synchronization when used in
synchronized systems. Another technical advantage presented by the
invention is that a low data rate system is provided, but without
substantially compromising the quality of the speech, as is characteristic
with low data rate systems heretofore known. Yet another technical
advantage of the invention is that a very low data rate can be achieved by
eliminating the processing and encoding of certain frames of speech
information, if the neighboring frames are characterized by the
substantially same information. A few bits are then transmitted to the
receiver for enabling the reproduction of the neighboring frame
information, whereupon the processing and transmission of the redundant
speech information is eliminated, and the bit rate can be minimized. A
further technical advantage of the invention is that the processing time,
or latency, required to encode the speech information at a low data rate
is lower than systems heretofore known, and is low enough such that
interactive bidirectional communications are possible.
The foregoing technical advantages of the invention are realized by the
profile encoding of scalar vector representations of energy, voicing and
pitch information of the speech signals. Each scalar is quantized
separately over ten frames which comprise a block. A time profile of the
speech information is thereby provided.
According to the speech encoder of the invention, speech information is
digitized to form frames of speech data having voicing, pitch, energy and
spectrum information. Each of the speech parameters are vector quantized
to achieve a profile encoding of the speech information. A fixed data rate
system is achieved by transmitting the speech parameters in ten-frame
blocks. Each 300 millisecond block of speech is represented by 120 bits
which are allocated to the noted parameters. Advantage is taken of the
spectral dynamics of the speech information by transmitting the spectrum
in ten-frame blocks and by replacing the spectral identity of two frames
which may be best interpolated by neighboring frames.
A codebook for spectral quantization is created using standard clustering
algorithms, with clustering being performed on principal spectral
component representations of a linear predictive coding model. Standard
KMEANS clustering algorithms are utilized. Spectral data reduction within
each N frame block is achieved by substituting interpolated spectral
vectors for the actual codebook values whenever such interpolated values
closely represent the desired values. Then, only the frame index of the
interpolated frames need be transmitted, rather than the complete ten-bit
codebook values.
BRIEF DESCRIPTION OF THE DRAWINGS
Further features and advantages will become apparent from the following and
more particularly description of the preferred embodiment of the
invention, as illustrated in the accompanying drawings in which like
reference characters generally refer to the same parts or elements
throughout the views, and in which:
FIG. 1 illustrates an environment in which the present invention may be
advantageously practiced;
FIG. 2 is a block diagram illustrating the functions of the speech encoder
of the invention; and
FIG. 3 illustrates the format for encoding speech information according to
various parameters.
DETAILED DESCRIPTION OF THE INVENTION
FIG. 1 illustrates an application of the invention utilized in connection
with underwater or marine transmission. Because of such medium for
transmitting information from one location to another, the data rate is
limited to very low rates, e.g., 200-800 bits per second. Speech
information is input to the transmitter portion of the marine transmission
system via a microphone 10. The analog audio information is converted into
digital form by digitizer 12, and then input to a speech encoder 14. The
encoding of the digital information according to the invention will be
described in more detail below. The output of the encoder 14 is
characterized as digital information transmittable at a very low data
rate, such as 400 bits per second. The digital output of the encoder 14 is
input to a transducer 16 for converting the low speed speech information
for transmission through the marine medium.
The low speed transmission of speech through the marine medium is received
at a remote location by a receiver transducer 18 which transforms the
encoded speech information into corresponding electrical representations.
A decoder or synthesizer 20 receives the electrical signals and conducts a
reverse transformation for converting the same into digital speech
information. A digital-to-analog converter 22 is effective to convert the
digital speech information into analog audio information corresponding to
the speech information input into the microphone 10. Such a system
constructed in accordance with the invention allows the speech signals to
be transmitted and received using a very low bit rate, and without
substantially affecting the quality of the speech information. Also, the
throughput of the system, from transmitter to receiver, is sufficiently
high as to enable the system to be interactive. In other words, the
bidirectional transmission and receiving of speech information can be
employed in real time so that the latency time is sufficiently short so as
not to confuse the speakers and listeners.
With reference now to FIG. 2, there is illustrated a simplified block
diagram of the invention, according to the preferred embodiment thereof.
Included in the transmission portion of the system is an analog amplifier
26 for amplifying speech signals and applying the same to an
analog-to-digital converter 28. The A/D converter 28 samples the input
speech signals at a 8 kHz rate and produces a digital output
representative of the amplitude of each sample. While not shown, the
speech A/D converter 28 includes a low pass filter for passing only those
audio frequencies below about 4 kHz. The digital signals generated by the
A/D converter 28 are buffered to temporarily store the digital values for
subsequent processing. Next, the series of digitized speech signals are
coupled to a linear predictive coding (LPC) analyzer 30 to produce LPC
vectors associated with 20 millisecond frame segments. The LPC analyzer 30
is of conventional design, including a signal processor programmed with a
conventional algorithm to produce the LPC vectors.
According to conventional LPC analysis, the speech characteristics are
assumed to be nonchanging, in a statistical sense, over short periods of
time. Thus, 20 millisecond periods are selected to define frame periods to
process the voice information. The LPC analyzer 30 provides an output
comprising LPC coefficients representative of the analog speech input. In
practice 10 LPC coefficients characteristic of the speech signals are
output by the analyzer 30. Linear predictive coding analysis techniques
and methods of programming thereof are disclosed in a text entitled,
Digital Processing of Speech Signals, by L. R. Rabiner and R. W. Schafer,
Prentice Hall Inc., Inglewood Cliffs, N.J., 1978, Chapter 8 thereof. The
subject matter of the noted text is incorporated herein by reference.
According to LPC processing, a model of the speech signals is formed
according to the following equation:
X.sub.n =a.sub.1 x.sub.n-1 +a.sub.2 x.sub.n-2 +. . . +a.sub.p x.sub.n-p
where x are the sample amplitudes and a.sub.1 -a.sub.p are the
coefficients. In essences, the "a" coefficients describe the system model
whose output is known, and the determination is to be made as to the
characteristics of a system that produced such output. According to
conventional linear predictive coding analysis, the coefficients are
determined such that the squared differences, or euclidean distance,
between the actual speech sample and the predicted speech sample is
minimized. Reflection coefficients are derived which characterize the "a"
coefficients, and thus the system model. The reflection coefficients
generally designated by the alphabet "k", identify a system whose output
is:
a.sub.0 =k.sub.1 a.sub.1 +k.sub.2 a.sub.2 . . . k.sub.10 a.sub.10.
An LPC analysis predictor is thereby defined with the derived reflection
coefficient value of the digitized speech signal.
The ten linear predictive coding reflection coefficients of each frame are
then output to a filter bank 32. In accordance with conventional
techniques, the filter bank transforms the LPC coefficients into spectral
amplitudes by measuring the response of the input LPC inverse filter at
specific frequencies. The frequencies are spaced apart in a logarithmic
manner. After the amplitudes have been computed by the filter bank 32, the
resulting amplitude vectors are rotated and scaled so that the transformed
parameters are statistically uncorrelated and exhibit an identity
covariance matrix. This is illustrated by block 34 of FIG. 2. The
statistically uncorrelated parameters comprise the principal spectral
components (PSC's) of the analog speech information. A euclidean distance
in this feature space is then utilized as the metric to compare test
vectors with a codebook 38, also comprising vectors. The system arranges
the frames in blocks of ten and processes the speech information according
to such blocks, rather than according to frames, as was done in the prior
art. Each of the scalar vectors of energy, voicing and pitch is then
separately vector quantized, as noted below:
##EQU1##
As can be seen, a quantized energy vector is computed using the energy of
the each of the ten frames. In like manner, voice and pitch vectors are
also computed using the voice and pitch parameters of the ten frames. Each
of the noted vectors is quantized by considering time as the vector index.
In other words, the vector of each of the noted speech parameters is
formed starting with the first parameter of interest of the first frame
and proceeding to the tenth frame of the block. This procedure essentially
quantizes a time profile of each of the noted parameters. As noted, the
pitch and energy vectors are computed using the average values of the
pitch and energy parameters of each frame.
It can be seen from the foregoing that the block coding is conducted over a
number of frames, irrespective of the phonemic boundaries or transition
points of the speech sounds. In other words, the coding is conducted for N
frames in a block in a routine manner, without necessitating the use of
additional specialized algorithms or equipment to determine phonemic
boundaries. Next, the spectral vector quantization euclidean distance is
compared with a principal spectral component codebook 38, as noted in FIG.
2. The speech encoder of the invention includes a codebook of principal
spectral components, rather than prestored LPC vectors, as was done in
prior art techniques. The use of principal spectral components as a
distance metric improves performance by tailoring features to the
statistics of speech production, speaker differences, acoustical
environments, channel variations, and thus human speech perception. As a
result, the vector quantization process becomes far more stable and
versatile under conditions usually catastrophic for vector quantization
systems that utilize the LPC likelihood ratio as a distance measure.
The codebook for spectral quantization is developed using standard
clustering algorithms, with clustering being performed on the principal
spectral component representations of the LPC model. In the preferred form
of the invention, a standard KMEANS clustering algorithm is utilized, each
cluster being represented in two forms. First, for the purpose of
iterating the clustering procedure and for subsequently performing the
vector quantization in the speech coding process (transmitter), each
cluster is represented by a PSC minimax element of the cluster. The
minimax element of a cluster is essentially the cluster element for which
the distance to the most remote element in the cluster is minimized. Each
cluster is also represented by a set of LPC model parameters, where this
model is produced by averaging all cluster elements in the audio
correlation domain. This LPC model is employed by the speech decoder
(receiver) to resynthesize the speech signal.
Spectral data reduction within each N frame block is achieved by
substituting interpolating spectral vectors for the actual codebook values
whenever such interpolated values closely represent the desired values.
Then, only the frame index of these interpolated values needs to be
transmitted, rather than the complete ten-bit codebook values. For
example, if it is required that M frames be interpolated, then the
distance between the spectral vector for frame k,S(k), and its
interpolated value, S.sub.int (k), is computed according to the following
equation:
D.sub.int (k)=.vertline..vertline.S(k)-S.sub.int (k).vertline..vertline.,
where
S.sub.int (k)=0.5* [S.sub.vq (k-1)].
The M values of k for which D.sub.int (k) is minimized are selected as the
interpolated frames, where k ranges from 2 to N-1, subject to the
restriction that adjacent frames are not allowed to be interpolated. As a
typical example, if N is ten and M is two, then there are twenty-one
possible pairs of interpolated frames per blocks, and the number of bits
required to encode the indices of the interpolated frames is therefore
five (2.sup.5 =32). Block encoding is also employed for encoding
excitation information. For encoding the voicing information, a histogram
can be computed for all 1024 possible voicing vectors. The voicing vector
consists of a sequence of ten ones and zeros indicating voice or unvoiced
frames. Many of the vectors are quite improbable, and thus the development
of a smaller size codebook is possible (e.g., containing only 128
vectors). The size of the final codebook can be determined by the entropy
of the full codebook. The Table below illustrates a partial histogram of
voicing codebook entries, rank-ordered in decreasing frequency of
occurrence. The Table illustrates that the average number of bits of
information per ten-frame block is 5.755.
TABLE
______________________________________
LIKELIHOOD PROFILE
______________________________________
0.200 1111111111
0.107 0000000000
0.028 0111111111
0.028 1111111110
0.028 0011111111
0.027 1111111100
0.024 0001111111
0.024 1111111000
0.018 1111110000
0.018 0000111111
0.014 1111100000
0.013 0000011111
0.012 1110001111
0.011 1111000111
______________________________________
Note that 3.3 bits are required to perform a complete time indexing of the
voicing events to locate an event within a ten-frame block. If, for
example, it is anticipated to expend 8 bits on voicing block coding (0.8
bits/frame), then the entropy is under 6 bits per block, thus indicating
additional potential savings if a Huffman coding is employed. The distance
metric used to compare an input voicing vector with the codebook is a
perceptually motivated extension of the Hamming distance. Experimentation
with this codebook has verified that the voicing information is retained
almost intact.
This method of encoding voice information is instrumental in reducing the
necessary bit assignment for encoding the pitch. The pitch is also
considered in vectors of length ten, and the unvoiced sections within that
vector are eliminated by "bridging" the voiced sections. In particular, if
there is an unvoiced section at the beginning or end of the vector, the
closest nonzero pitch value is repeated, while an unvoiced section in the
middle of the vector is assigned pitch values by interpolating the pitch
at the two ends of the section. This method of bridging is successful
because the pitch contour demonstrates a very slowly changing behavior,
and thus the final vectors are smooth. The pitch is represented
logarithmically, and the bridging is also conducted in the logarithmic
domain. Once the whole vector is made to represent voiced and
pseudo-voiced frames, the contour is normalized by subtracting from the
log (pitch) values and their average, log(P). In other words, P represents
the geometric mean of the pitch values. In this way, the vectors
correspond to different pitch contour patterns, and they are not dependant
on the average pitch level of the speaker. Log(P) is quantized separately
by a scalar quantizer, and the quantized value is utilized in
normalization. A pitch vector is then vector quantized, with a distance
metric that gives heavier weight to the voiced sections than to the
unvoiced sections. Typical bit allocations for pitch quantization are four
bits for block quantization and nine bits for vector quantizing the pitch
profile.
Encoding of the energy is performed in a manner analogous to that for pitch
and voicing. The individual energy frames within the ten-frame block are
first normalized by the average preemphasized RMS frame energy within the
block, designated by E.sub.norm. Then, a pseudo-logarithmic conversion of
the normalized frame energy, E(k), is performed, where
E.sub.p1 (k)=LOG[1+Beta*E(k)/E.sub.norm ].
This nonlinear transformation preserves the perceptually important dynamic
range characteristics in the vector quantization process which defines the
euclidean distance metric for use in the invention. The resulting
ten-frame vector of the normalized and transformed energy profile is then
vector quantized. Typical bit allocations for energy quantizations are
four bits for block normalization and ten bits for vector quantizing the
energy profile.
The bit allocation for each block of ten frames is illustrated in FIG. 3.
As noted, the voicing requires eight bits per block, the pitch requires
thirteen bits per block, the energy parameter requires fourteen bits per
block and the spectrum requires eighty-five bits per block. There are thus
120 bits per ten-frame block which are calculated every 300 milliseconds.
Further, for each one second period, 400 bits are output by the digital
transmitter 40.
The encoder of the invention may further employ apparatus or an algorithm
for discarding frames of information, the speech information of which is
substantially similar to adjacent frames. For each frame of information
discarded, an index or flag signal is transmitted in lieu thereof to
enable the receiver to reinsert decoded signals of the similar speech
information. By employing such a technique, the transmission data rate can
be further decreased, in that there are fewer bits comprising the flag
signals than there are comprising the speech information. The similarity
or "informativeness" of a frame of speech information is determined by
calculating an euclidean distance between adjacent frames. More
specifically, the distance is calculated by finding an average of the
frames on each side of a frame of interest, and use the average as an
estimator. The similarity of a frame of interest and the estimator is an
indication of the "informativeness" of the frame of interest. When each
frame is averaged in the manner noted, if its informativeness is below a
predefined threshold, then the frame is discarded. On the other hand, if a
large euclidean distance is found, the frame is considered to contain
different or important speech information not contained in neighboring
frames, and thus such frame is retained for transmission.
With reference again to FIG. 2, the receiver section of the very low rate
speech decoder includes a spectrum vector selector 42 operating in
conjunction with an LPC decode-book 44. The vector selector 42 and
decode-book 44 function in a manner similar to that of the transmitter
blocks 36 and 38, but rather decode the transmitted digital signals into
other signals utilizing the LPC decode-book 44. Transmitted along with the
encoded speech information are other signals for use by the receiver in
determining which frames have been discarded, as being substantially
similar to neighboring frames. With this information, the spectrum vector
selector utilizes the LPC decode-book 44 for outputting a digital code in
the frame time slots which were discarded in the receiver.
Functional block 46 illustrates an LPC synthesizer, including a
digital-to-analog converter (not shown) for transforming the decoded
digital signals into audio analog signals. The resynthesis of the digital
signals output by the spectrum vector selector 42 are not as easily
regenerated by a function which is the converse of that required for
encoding the speech information in the transmitter section. The reason for
this is that there is no practical method of extracting the PSC components
from the LPC parameters. In other words, no inverse transformation exists
for converting PSC vectors back into LPC vectors. Therefore, the decoding
is completed by utilizing the vector P.sub.j from the cluster of a number
of P.sub.j 's from which the .vertline.X.sub.j 31 X.sub.k .vertline. is
minimum. In other words, the euclidean distance between the X and the
reference X, e.g., the average of all the cluster values, is minimum.
In the alternative, and having available the X.sub.j components, the
P.sub.j vectors are obtained by utilizing the P.sub.k vectors for which
the maximum distance between .vertline.X.sub.i -X.sub.j .vertline. over
all i in the set of the cluster values is a minimum. The minimax is
determined, taking the maximum distance between any X.sub.i in the
selected X.sub.j, and selecting the i for which it is minimum.
The time involved in the transmitter and receiver sections of the very low
bit rate transmission system in encoding and decoding the speech
information is in the order of a half second. This very low latency index
allows the system to be interactive, i.e., allows speakers and listeners
to communicate with each other without incurring long periods of
processing time required for processing the speech information. Of course,
with such an interactive system, two transmitters and receivers would be
required for transmitting and receiving the voice information at remote
locations.
From the foregoing, a very low bit rate speech encoder and decoder have
been disclosed for providing enhanced communications at low data rates.
While the preferred embodiment of the invention has been disclosed with
reference to a specific speech encoder and decoder apparatus and method,
it is to be understood that many changes in detail may be made as a matter
of engineering choices without departing from the spirit and scope of the
invention, as defined by the appended claims.
* * * * *
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