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BACKGROUND AND SUMMARY OF THE INVENTION
The present invention relates to voice coding systems.
A very large range of applications exists for voice coding systems,
including voice mail in microcomputer networks, voice mail sent and
received over telephone lines by microcomputers, user-programmed synthetic
speech, etc.
In particular, the requirements of many of these applications are quite
different from those of simple speech synthesis applications (such as a
Speak & Spell) (TM)), wherein synthetic speech can be carefully encoded
and then stored in a ROM or on disk. In such applications, high speed
computers with elaborate algorithms, combined with hand tweaking, can be
used to optimize encoded speech for good intelligibility and low bit
requirements. However, in many other requirements, the speech encoding
step does not have such large resources available. This is most obviously
true in voice mail microcomputer networks, but it is also important in
applications where a user may wish to generate his own reminder messages,
diagnostic messages, signals during program operation, etc. For example, a
microcomputer system wherein the user could generate synthetic speech
messages in his own software would be highly desirable, not only for the
individual user, but also for the software production houses which do not
have trained speech scientists available.
A particular problem in such applications is energy variation. That is, not
only will a speaker's voice intensity typically contain a large dynamic
range related to sentence inflection, but different speakers will have
different volume levels, and the same speaker's voice level may vary
widely at different times. Untrained speakers are especially likely to use
nonuniform uncontrolled variations in volume, which the listener normally
ignores. This large dynamic range would mean that the voice coding method
used must accommodate a wide dynamic range, and therefore an increased
number of bits would be required for coding at reasonable resolution.
However, if energy normalization can be used (i.e. all speech adjusted to
approximately a constant energy level) these problems are ameliorated.
Energy normalization also improves the intelligibility of the speech
received. That is, the dynamic range available from audio amplifiers and
loudspeakers is much less than that which can easily be perceived by the
human ear. In fact, the dynamic range of loudspeakers is typically much
less than that of microphones. This means that a dynamic range which is
perfectly intelligible to a human listener may be hard to understand if
communicated through a loudspeaker, even if absolutely perfect encoding
and decoding is used.
The problem of intelligibility is particularly acute with audio amplifiers
and loudspeakers which are not of extremely high fidelity. However,
compact low-fidelity loudspeakers must be used in most of the most
attractive applications for voice analysis/synthesis, for reasons of
compactness, ruggedness, and economy.
A further desideratum is that, in many attractive applications, the person
listening to synthesized speech should not be required to twiddle a volume
control frequently. Where a volume control is available, dynamic range can
be analog-adjusted for each received synthetic speech signal, to shift the
narrow window provided by the loudspeaker's narrow dynamic range, but this
is obviously undesirable for voice mail systems and many other
applications.
In the prior art, analog automatic gain controls have been used to achieve
energy normalization of raw signals. However, analog automatic gain
controls distort the signal input to the analog to digital converter. That
is, where (e.g.) reflection coefficients are used to encode speech data,
use of an automatic gain control in the analog signal will introduce error
into the calculated reflection coefficients. While it is hard to analyze
the nature of this error, error is in fact introduced. Moreover, use of an
analog automatic gain control requires an analog part, and every
introduction of special analog parts into a digital system greatly
increases the cost of the digital system. If an AGC circuit having a fast
response is used, the energy levels of consecutive allophones may be
inappropriate. For example, in the word "six" the sibilant /s/ will
normally show a much lower energy than the vowel /i/. If a fast-response
AGC circuit is used, the energy-normalized-word "six" is left with a sound
extremely hissy, since the initial /s/ will be raised to the same energy
as the i/i/, inappropriately. Even if a slower-response AGC circuit is
used, substantial problems still may exist, such as raising the noise
floor up to signal levels during periods of silence, or inadequate
limiting of a loud utterance following a silent period.
Thus it is an object of the present invention to provide a digital system
which can perform energy normalization of voice signals.
It is further object of the present invention to provide a method for
energy normalization of voice signals which will not overemphasize initial
constants.
It is a further object of the present invention to provide a method for
energy normalization of voice signals which can rapidly respond to energy
variations in a speaker's utterance, without excessively distorting the
relative energy levels of adjacent allophones with an utterance.
A further general problem with energy normalization is caused by the
existence of noise during silent periods. That is, if an energy
normalization system brings the noise floor up towards the expected normal
energy level during periods when no speech signal is present, the
intelligibility of speech will be degraded and the speech will be
unpleasant to listen to. In addition, substantial bandwidth will be wasted
encoding noise signals during speech silence periods.
It is a further object of the present invention to provide a voice coding
system which will not waste bandwidth on encoding noise during silent
periods.
The present invention solves the problems of energy normalization
digitally, by using look-ahead energy normalization. That is, an adaptive
energy normalization parameter is carried from frame to frame during a
speech analysis portion of an analysis-synthesis system. Speech frames are
buffered for a fairly long period, e.g. 1/2 second, and then are
normalized according to the current energy normalization parameter. That
is, energy normalization is "look ahead" normalization in that each frame
of speech (e.g. each 20 millisecond interval of speech) is normalized
according to the energy normalization value from much later, e.g. from 25
frames later. The energy normalization value is calculated for the frames
as received by using a fast-rising slow-falling peak-tracking value.
In a further aspect of the present invention, a novel silence suppression
scheme is used. Silence suppression is achieved by tracking 2 additional
energy contours. One contour is a slow-rising fast-falling value, which is
updated only during unvoiced speech frames, and therefore tracks a lower
envelope of the energy contour. (This in effect tracks the ambient noise
level.) The other parameter is a fast-rising slow-falling parameter, which
is updated only during voiced speech frames, and thus tracks an upper
envelope of the energy contour. (This in effect tracks the average speech
level.) A threshold value is calculated as the maximum of respective
multiples of these 2 parameters, e.g. the greater of: (5 times the lower
envelope parameter), and (one fifth of the upper envelope parameter).
Speech is not considered to have begun unless a first frame which both has
an energy above the threshold level and is also voiced is detected. In
that case, the system then backtracks among the buffered frames to include
as "speech" all immediately preceding frames which also have energy
greater than the threshold. That is, after a period during which the
frames of parameters received have been identified as silent frames, all
succeeding frames are tentively identified as silent frames, until a
super-threshold-energy voiced frame is found. At that point, the silence
suppression system backtracks among frames immediately preceding this
super-threshold energy voiced frame until an broken string
subthreshold-energy frames at least to 0.4 seconds long is found. When
such a 0.4 second interval of silence is found, backtracking ceases, and
only those frames after the 0.4 seconds of silence and before the first
voiced super-threshold energy frame are identified as non-silent frames.
At the end of speech, when a voiced frame is detected having an energy
below the threshold T, a waiting counter is started. If the waiting
reaches an upper limit (e.g. 0.4 seconds), without the energy again
increasing above T, the utterance is considered to have stopped. The
significance of the speech/silence decision is that bits are not wasted on
encoding silent frames, energy tracking is not distorted by the presence
of silent frames as discussed above, and long utterances can be input from
an untrained speakers, who are likely to leave very long silences between
consecutive words in a sentence.
According to the present invention there is provided:
A speech coding system, comprising:
An analyzer connected to receive a digital speech signal and to generate
therefrom a sequence of frames of speech parameters, said parameters of
each frame including an energy value, and
means for normalizing the energy value of each said speech frame with
respect to energy values of subsequent frames; and
output means for loading said parameters for each said speech frame
including said normalized energy parameter of each said speech frame into
a data channel.
According to the present invention there is provided:
A method of encoding speech, comprising the steps of:
Analyzing a speech signal to provide a sequence of frames as speech
parameters, each said frame of said sequence of parameters including an
energy value;
normalizing said energy values of each of said speech frames with respect
to energy values of subsequent ones of said speech frames; and
encoding said speech parameters including said normalized ones of said
energy values into a data channel.
BRIEF DESCRIPTION OF THE DRAWINGS
The present invention will be described with reference to the accompanying
drawings, which are hereby incorporated by reference and attested to by
the attached Declaration, wherein:
FIG. 1 shows one aspect of the present invention, wherein an adaptively
normalized energy level ENORM is derived from the successive energy levels
of a sequence of speech frames;
FIG. 2 shows a further aspect of the present invention, wherein a
look-ahead energy normalization curve ENORM* is used for normalization;
FIG. 3 shows a further aspect of the present invention, used in silence
suppression, wherein high and low envelope curves are continuously
maintained for the energy values of a sequence of speech input frames;
FIG. 4 shows a further aspect of the invention, wherein the EHIGH and ELOW
curves of FIG. 3 are used to derive a threshold curve T; and
FIG. 5 shows a sample system configuration for practicing the present
invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
The present invention provides a novel speech analysis/synthesis system,
which can be configured in a wide variety of embodiments. However, the
presently preferred embodiment uses a VAX 11/780 computer, coupled with a
Digital Sound Corporation Model 200 A/D and D/A converter to provided
high-resolution high-bit-rate digitizing and to provide speech synthesis.
Naturally, a conventional microphone and loudspeaker, with an analog
amplifier such as a Digital Sound Corporation Model 240, are also used in
conjunction with the system.
However, the present invention contains novel teachings which are also
particularly applicable to microcomputer-based systems. That is, the high
resolution provided by the above digitizer is not necessary, and the
computing power available on the VAX is also not necessary. In particular,
it is expected that a highly attractive embodiment of the present
invention will use a TI Professional Computer (TM), using the built in
low-quality speaker and an attached microphone as discussed below.
The system configuration of the presently preferred embodiment is shown
schematically in FIG. 5. That is, a raw voice input is received by
microphone 10, amplified by microphone amplifier 12, and digitized by D/A
converter 14. The D/A converter used in the presently preferred
embodiment, as noted, is an expensive high-resolution instrument, which
provides 16 bits of resolution at a sample rate of 8 kHz. The data
received at this high sample rate will be transformed to provide speech
parameters at a desired frame rate. In the presently preferred embodiment
the frame rate is 50 frames per second, but the frame period can easily
range between 10 milliseconds and 30 milliseconds, or over an even wider
range.
In the presently preferred embodiment, linear predictive coding based
analysis is used to encode the speech. That is, the successive samples (at
the original high bit rate, of, in this example, 8000 per second) are used
as inputs to derive a set of linear predictive coding parameters, for
example 10 reflection coefficants k.sub.1 -k.sub.10 plus pitch and energy,
as described below.
In practicing the present invention, the audible speech is first translated
into a meaningful input for the system. For example, a microphone within
range of the audible speech is connected to a microphone preamplifier and
to an analog-to-digital converter. In the presently preferred embodiment,
the input stream is sampled 8000 times per second, to an accuracy of 16
bits. The stream of input data is then arbitrarily divided up into
successive "frames", and, in the presently preferred embodiment, each
frame is defined to include 160 samples. That is, the interval between
frames is 20 msec, but the LPC parameters of each frame are calculated
over a range of 240 samples (30 msec).
In one embodiment, the sequence of samples in each speech input frame is
first transformed into a set of inverse filter coefficients a.sub.k, as
conventionally defined. See, e.g., Makhoul, "Linear Prediction: A Tutorial
Review", proceedings of the IEEE, Volume 63, page 561 (1975), which is
hereby incorporated by reference. That is, in the linear prediction model,
the a.sub.k 's are the predictor coefficients with which a signal S.sub.k
in a time series can be modeled as the sum of an input u.sub.k and a
linear combination of past values S.sub.k-n in the series. That is:
##EQU1##
Each input frame contains a large number of sampling points, and the
sampling points within any one input frame can themselves be considered as
a time series. In one embodiment, the actual derivation of the filter
coefficients a.sub.k for the sample frame is as follows: First, the
time-series autocorrelation values R.sub.i are computed as
##EQU2##
where the summation is taken over the range of samples within the input
frame. In this embodiment, 11 autocorrelation values are calculated
(R.sub.0 -R.sub.10). A recursive procedure is now used to derive the
inverse filter coefficients as follows:
##EQU3##
These equations are solved recursively for: i=1, 2, . . . , up to the model
order p (p=10 in this case). The last iteration gives the final a.sub.k
values.
The foregoing has described an embodiment using Durbin's recursive
procedure to calculate the a.sub.k 's for the sample frame. However, the
presently preferred embodiment uses a procedure due to Leroux-Gueguen. In
this procedure, the normalized error energy E (i.e. the self-residual
energy of the input frame) is produced as a direct byproduct of the
algorithm. The Leroux-Gueguen algorithm also produces the reflection
coefficients (also referred to as partial correlation coefficients)
k.sub.i. The reflection coefficients k.sub.r are very stable parameters,
and are insensitive to coding errors (quantization noise).
The Leroux-Gueguen procedure is set forth, for example, in IEEE
Transactions on Acoustic Speech and Signal Processing, page 257 (June
1977), which is hereby incorporated by reference. This algorithm is a
recursive procedure, defined as follows:
##EQU4##
This algorithm computes the reflection coefficients k.sub.i using as
intermediaries impulse response estimates e.sub.k rather then the filter
coefficients a.sub.k.
Linear predictive coding models generally are well known in the art, and
can be found extensively discussed in such references as Rabiner and
Schafer, Digital Processing of Speech Signals (1978), Markel and Gray,
Linear Predictive Coding of Speech (1976), which are hereby incorporated
by reference, and in many other widely available publications. It should
be noted that the excitation coding transmitted need not be merely energy
and pitch, but may also contain some additional information regarding a
residual signal. For example, it would be possible to encode a bandwidth
of the residual signal which was an integral multiple of the pitch, and
approximately equal to 1000 Hz, as an excitation signal. Such a technique
is extensively discussed in Patent Application Ser. No. 484,720, filed
Apr. 13, 1983, which is hereby incorporated by reference. Many other
well-known variations of encoding the excitation information can also be
used alternatively. Similarly, the LPC parameters can be encoded in
various ways. For example, as is also well known in the art, there are
numerous equivalent formulations of linear predictive coefficients. These
can be expressed as the LPC filter coefficients a.sub.k, or as the
reflection coefficients k.sub.i, or as the autocorrelations R.sub.i, or as
other parameter sets such as the impulse response estimates parameters
E(i) which are provided by the LeRoux-Guegen procedure. Moreover, the LPC
model order is not necessarily 10, but can be 8, 12, 14, or other.
Moreover, it should be noted that the present invention does not
necessarily have to be used in combination with an LPC speech encoding
model at all. That is, the present invention provides an energy
normalization method which digitally modifies only the energy of each of a
sequence of speech frames, with regard to only the energy and voicing of
each of a sequence of speech frames. Thus, the present invention is
applicable to energy normalization of the systems using any one of a great
variety of speech encoding methods, including transform techniques,
formant encoding techniques, etc.
Thus, after the input samples have been converted to a sequence of speech
frames each having a data vector including an energy value, FIG. 5, item
16) the present invention operates on the energy value of the data
vectors. In the presently preferred embodiment, the encoded parameters are
the reflection coefficients k.sub.1 -k.sub.10, the energy, and pitch. (The
pitch parameter includes the voicing decision, since an unvoiced frame is
encoded as pitch=zero.)
The novel operations in the system of the present invention begin at this
point. That is, a sequence of encoded frames, each including an energy
parameter and modeling parameters, is provided as the raw output of the
speech analysis section. Note that, at this stage, the resolution of the
energy parameter coding is much higher than it will be in the encoded
information which is actually transmitted over the communications or
storage channel 40. The way in which the present invention performs energy
normalization on successive frames, and suppresses coding of silent
frames, may be seen with regard to the energy diagrams of FIGS. 1-4. These
show examples of the energy values E(i) seen in successive frames i within
sequence of frames, as received as raw output in the speech analysis
section.
An adaptive parameter ENORM(i) is then generated, approximately as shown in
FIG. 1. That is, ENORM(0) is an initial choice for that parameter, e.g.
ENORM(0)=100. ENORM is subsequently updated, for each successive frame, as
follows:
If E(i) is greater than ENORM(i-1), then ENORM (i) is set equal to alpha
times E(i)+(1-alpha) times ENORM(i-1);
Otherwise, ENORM(i) is set equal to beta times E(i)+(1-beta) times
ENORM(i-1),
where alpha is given a value close to 1 to provide a fast rising time
constant (preferably about 0.1 seconds), and Beta has given a value close
to 0, to provide a slow falling time constant (preferably in the
neighborhood of 4 seconds).
This adaptive peak-tracking parameter ENORM(i) is used to normalize the
energy of the frames, but this not done directly. The energy of each frame
I is normalized by dividing it by a look ahead normalized energy
ENORM*(i), where ENORM*(i) is defined to be equal to ENORM(i+d), where d
represents a number of frames of delay which is typically chosen to be
equivalent to 1/2 second (but may be in the range of 0.1 to 2 seconds, or
even have values outside this range). Thus, the energy E(i) of each frame
is normalized by dividing by the normalized energy ENORM*(i):
E*(i) is set equal to E(i)/ENORM*(i).
This is accomplished by buffering a number of speech frames equal to the
delay d, so that the value of ENORM for the last frame loaded into the
buffer provides the value of ENORM* for the oldest frame in the buffer,
i.e. for the frame currently being taken out of the buffer.
The introduction of this delay in the energy normalization means that the
energy of initial low-energy periods will be normalized with respect to
the energy of immediately following high-energy periods, so that the
relative energy of initial consonants will not be distorted. That is,
unvoiced frames of speech will typically have an energy value which is
much lower than that of voiced frames of speech. Thus, in the word "six"
the initial allophone/s/ should be normalized with respect to the energy
level of the vowel allophone /i/. If the allophone /s/ is normalized with
respect to its own energy, then it will be raised to an improperly high
energy, and the initial consonant /s/ will be greatly overemphasized.
Since the falling time constant (corresponding to the parameter beta) is so
long, energy normalization at the end of a word will not be distorted by
the approximately zero-energy value of the following frames of silence.
(In addition, when silence suppression is used, as is preferable, the
silence suppression will prevent ENORM from falling very far in this
situation.) That is, for a final unvoiced consonant, the long time
constant corresponding to beta will means that the energy normalization
value ENORM of the silent frames 1/2 second after the end of a word will
be still be dominated by the voiced phonemes immediately preceding the
final unvoiced consonant. Thus, the final unvoiced constant will be
normalized with respect to preceeding voiced frames, and its energy also
will not be unduly raised. Normalization is done in FIG. 5, items 22 and
24. Determination of voiced or unvoiced frames is done in item 18.
Thus, the foregoing steps provide a normalized energy E*(i) for each speech
frame i. In the presently preferred embodiment, a further novel step is
used to suppress silent periods. As shown in the diagram of FIG. 5,
silence detection is used to selectively prevent certain frames from being
encoded (item 20). Those frames which are encoded are encoded with a
normalized energy E*(i), together with the remaining speech parameters in
the chosen model (which in the presently preferred embodiment are the
pitch P and the reflection coefficients k.sub.1 -k.sub.10).
Silence suppression is accomplished in a further novel aspect of the
present invention, by carrying 2 envelope parameters: ELOW and EHIGH. Both
of these parameters are started from some initial value (e.g. 100) and
then are updated depending on the energy E(i) of each frame i and on the
voiced or unvoiced status of that frame. If the frame is unvoiced, then
only the lower parameter ELOW is updated as follows:
If E(i) is greater than ELOW, then ELOW is set equal to gamma times
E(i)+(1-gamma) times ELOW;
otherwise, ELOW is set equal to delta times E(i)+(1-delta) times ELOW,
where gamma corresponds to a slow rising time constant (typically 1
second), and delta corresponds to a fast falling time constant (typically
0.1 second). Thus, ELOW in effect tracks a lower envelope of the energy
contour of EI. The parameters gamma and delta are referred to in the
accompanying software as ALOWUP and ALOWDN.
If the frame I is voiced, then only EHIGH is updated, as follows:
If E(i) is greater then EHIGH, the EHIGH is set equal to epsilon times
E(i)+(1-epsilon) times EHIGH;
otherwise, EHIGH is set equal to zeta times E(i)+(1-zeta) times EHIGH,
where epsilon corresponds to a fast rising time constant (typically 0.1
seconds), and zeta corresponds to a fast falling time constant (typically
1 second). Thus, EHIGH tracts an upper envelope of the energy contour. The
parameters ELOW and EHIGH are shown in FIG. 3. Note that the parameter
EHIGH is not updated during the initial unvoiced series of frames, and the
parameter ELOW is not disturbed during the following voiced series of
frames.
The 2 envelope parameters ELOW and EHIGH are then used to generate 2
threshold parameters TLOW and THIGH, defined as:
TLOW=PL times ELOW
THIGH=PH times EHIGH,
where PL and PH are scaling factors (e.g. PL=5 and PH=0.2). A threshold T
is then set as the maximum of TLOW and THIGH.
Based on this threshold T, a decision is made whether a frame is nonsilent
or silent, as follows:
If the current frame is a silent frame, all following frames will be
tentatively assumed to be silent unless a voiced super-threshold-energy
(and therefore nonsilent) frame is detected. The frames tentatively
assumed to be silent will be stored in a buffer (preferable containing at
least one second of data), since they may be identified later as not
silent. A speech frame is detected only when some frame is found which has
a frame energy E(i) greater than the threshold T and which is voiced. That
is, an unvoiced super-threshold-energy frame is not by itself enough to
cause a decision that speech has begun. However, once a voiced high energy
frame is found, the prior frames in the buffer are reexamined, and all
immediately preceding unvoiced frames which have an energy greater than T
are then identified as nonsilent frames. Thus, in the sample word "six",
the unvoiced super-threshold-energy frames in the constant /s/ would not
immediately trigger a decision that a speech signal had begun, but, when
the voiced super-threshold-energy frames in the /i/ are detected, the
immediately preceding frames are reexamined, and the frames corresponding
to the /s/ which have energy greater than T are then also designated as
"speech" frames.
If the current frame is a "speech" (nonsilent) frame, the end of the word
(i.e. the beginning of "silent" frames which need not be encoded) is
detected as follows. When a voiced frame is found which has its energy
E(i) less than T, a waiting counter is started. If the waiting reaches an
upper limit (e.g. 0.4 seconds) without the energy ever rising above T,
then speech is determined to have stopped, and frames after the last frame
which had energy E(i) greater than T are considered to be silent frames.
These frames are therefore not encoded.
It should be noted that the energy normalization and silence suppression
features of the system of the present invention are both dependant in
important ways on the voicing decision. It is preferable, although not
strictly necessary, that the voicing decision be made by means of a
dynamic programming procedure which makes pitch and voicing decisions
simultaneously, using an interrelated distance measure (item 18 in FIG.
5). Such a system is presently preferred, and is described in greater
detail in U.S. Patent Application Ser. No. 484,718, filed Apr. 13, 1983,
which is hereby incorporated by reference. It should also be noted that
this system tends to classify low-energy frames as unvoiced. This is
desirable.
The actual encoding can now be performed with a minimum bit rate. In the
presently preferred embodiment, 5 bits are used to encode the energy of
each frame, 3 bits are used for each of the ten reflection coefficients,
and 5 bits are used for the pitch. However, this bit rate can be further
compressed by one of the many variations of delta coding, e.g. by fitting
a polynomial to the sequence of parameter values across successive frames
and then encoding merely the coefficients of that polynomial, by simple
linear delta coding, or by any of the various well known methods. (FIG. 5,
item 26)
In a further attractive contemplated embodiment of the invention, an
analysis system as described above is combined with speech synthesis
capability, to provide a voice mail station, or a station capable of
generating user-generated spoken remainder messages. This combination is
easily accomplished with minimal required hardware addition. The encoded
output of the analysis section, as described above, is connected to a data
channel of some sort. This may be a wire to which an RS 232 UART chip is
connected, or may be a telephone line accessed by a modem, or may be
simply a local data buss which is also connected to a memory board or
memory chips, or may of course be any of a tremendous variety of other
data channels. Naturally, connection to any of these normal data channels
is easily and conveniently made two way, so that data may be received from
a communication channel or recalled from memory. Such data received from
the channel will thus contain a plurality of speech parameters, including
an energy value.
In the presently preferred embodiment, where LPC speech modeling is used,
the encoded data received from the data channel will contain LPC filter
parameters for each speech frame, as well as some excitation information.
In the presently preferred embodiment, the data vector for each speech
frame contains 10 reflection coefficients as well as pitch and energy. The
reflection coefficients configure a tense-order lattice filter, and an
excitation signal is generated from the excitation parameters and provided
as input to this lattice filter. For example, where the excitation
parameters are pitch and energy, a pulse, at intervals equal to the pitch
period, is provided as the excitation function during voiced frames (i.e.
during frames when the encoded value of pitch is non zero), and
pseudo-random noise is provided as the excitation function when pitch has
been encoded as equal to zero (unvoiced frames). In either case, the
energy parameter can be used to define the power provided in the
excitation function. The output of the lattice filter provides the
LPC-modeled synthetic signal, which will typically be of good intelligible
quality, although not absolutely transparent. This output is then
digital-to-analog converted, and the analog output of the d-a converter is
provided to an audio amplifier, which drives a loudspeaker or headphones.
In a further attractive alternative embodiment of the present invention,
such a voice mail system is configured in a microcomputer-based system. In
this embodiment, at Texas Instruments Professional Computer.TM. with a
speech board incorporated is used as a voice mail terminal. This
configuration uses a 8088-based system, together with a special board
having a TMS 320 numeric processure chip mounted thereon. The fast
multiple provided by the TMS 320 is very convenient in performing signal
processing functions. A pair of audio amplifiers for input and output is
also provided on the speech board, as is an 8 bit mu-law codec. The
function of this embodiment is essentially identical to that of the VAX
embodiment described above, except for a slight difference regarding the
converters. The 8 bit codec performs mu-law conversion, which is non
linear but provides enhanced dynamic range. A lookup table is used to
transform the 8 bit mu-law output provided from the codec chip into a 13
bit linear output. Similarly, in a speech synthesis operation, the linear
output of the lattice filter operation is pre-converted, using the same
lookup table, to an 8-bit word which will give an appropriate analog
output signal from the codec. This microcomputer embodiment also includes
an internal speaker, and a microphone jack.
A further preferred realization is the use of multiple micro-computer based
voice mail stations, as described above, to configure a
microcomputer-based voice mail system. In such a system, microcomputers
are conventionally connected in a local area network, using one of the
many conventional LAN protocalls, or are connected using PBX tilids. The
only slightly distinctive feature of this voice mail system embodiment is
that the transfer mechnizam used must be able to pass binary data, and not
merely ASCII data. As between microcomputer stations which have the voice
mail analysis/synthesis capabilities discussed above, the voice mail
operation is simply a straight forward file transfer, wherein a file
representing encoded speech data is generated by an analysis operation at
one station, is transferred as a file to another station, and then is
converted to analog speech data by a synthe | | |
