Note: Descriptions are shown in the official language in which they were submitted.
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DETECTING VOICED AND UNVOICED SPEECH USING BOTH ACOUSTIC
AND NONACOUSTIC SENSORS
TECHNICAL FIELD
The disclosed embodiments relate to the processing of speech signals,
BACKGROUND
The ability to correctly identify voiced and unvoiced speech is critical to
many speech applications including speech recognition, speaker verification,
noise
suppression, and many others. In a typical acoustic application, speech from a
human speaker is captured and transmitted to a receiver in a different
location. In
the speaker's environment there may exist one or more noise sources that
pollute
the speech signal, or the signal of interest, with unwanted acoustic noise.
This
makes it difficult or impossible for the receiver, whether human or machine,
to
understand the user's speech.
Typical methods for classifying voiced and unvoiced speech have relied
mainly on the acoustic content of microphone data, which is plagued by
problems
with noise and the corresponding uncertainties in signal content: This is
especially
problematic now with the proliferation of portable communication devices like
cellular telephones and personal digital assistants because, in many cases,
the
quality of service provided by the device depends on the quality of the voice
services offered by the device. There are methods known in the art for
suppressing the noise present in the speech signals, but these methods
demonstrate performance shortcomings that include unusually long computing
time, requirements for cumbersome hardware to perform the signal processing,
and distorting the signals of interest.
BRIEF DESCRIPTION OF THE FIGURES
Figure 1 is a block diagram of a NAVSAD system, under an embodiment.
Figure 2 is a block diagram of a PSAD system, under an embodiment.
Figure 3 is a block diagram of a denoising system, referred to herein as the
Pathfinder system, under an embodiment.
Figure 4 is a flow diagram of a detection algorithm for use in detecting
voiced and unvoiced speech, under an embodiment.
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Figure 5A plots the received GEMS signal for an utterance along with the
mean correlation between the GEMS signal and the Mic 1 signal and the
threshold
for voiced speech detection.
Figure 5B plots the received GEMS signal for an utterance along with the
standard deviation of the GEMS signal and the threshold for voiced speech
detection.
Figure 6 plots voiced speech detected from an utterance along with the
GEMS signal and the acoustic noise.
Figure 7 is a microphone array for use under an embodiment of the PSAD
system.
Figure 8 is a plot of dM versus d~ for several dd values, under an
embodiment.
Figure 9 shows a plot of the gain parameter as the sum of the absolute
values of H~(z) and the acoustic data or audio from microphone 1.
Figure 10 is an alternative plot of acoustic data presented in Figure 9.
In the figures, the same reference numbers identify identical or substantially
similar elements or acts.
Any headings provided herein are for convenience only and do not
necessarily affect the scope or meaning of the claimed invention.
DETAILED DESCRIPTION
Systems and methods for discriminating voiced and unvoiced speech from
background noise are provided below including a Non-Acoustic Sensor Voiced
Speech Activity Detection (NAVSAD) system and a Pathfinder Speech Activity
Detection (PSAD) system. The noise removal and reduction methods provided
herein, while allowing for the separation and classification of unvoiced and
voiced
human speech from background noise, address the shortcomings of typical
systems known in the art by cleaning acoustic signals of interest without
distortion.
Figure 1 is a block diagram of a NAVSAD system 100, under an
embodiment. The NAVSAD system couples microphones 10 and sensors 20 to at
least one processor 30. The sensors 20 of an embodiment include voicing
activity
detectors or non-acoustic sensors. The processor 30 controls subsystems
including a detection subsystem 50, referred to herein as a detection
algorithm,
and a denoising subsystem 40. Operation of the denoising subsystem 40 is
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described in detail in the Related Applications. The NAVSAD system works
extremely well in any background acoustic noise environment.
Figure 2 is a block diagram of a PSAD system 200, under an embodiment.
The PSAD system couples microphones 10 to at least one processor 30. The
processor 30 includes a detection subsystem 50, referred to herein as a
detection
algorithm, and a denoising subsystem 40. The PSAD system is highly sensitive
in
low acoustic noise environments and relatively insensitive in high acoustic
noise
environments. The PSAD can operate independently or as a backup to the
NAVSAD, detecting voiced speech if the NAVSAD fails.
Note that the detection subsystems 50 and denoising subsystems 40 of
both the NAVSAD and PSAD systems of an embodiment are algorithms controlled
by the processor 30, but are not so limited. Alternative embodiments of the
NAVSAD and PSAD systems can include detection subsystems 50 and/or
denoising subsystems 40 that comprise additional hardware, firmware, software,
and/or combinations of hardware, firmware, and software. Furthermore,
functions
of the detection subsystems 50 and denoising subsystems 40 may be distributed
across numerous components of the NAVSAD and PSAD systems.
Figure 3 is a block diagram of a denoising subsystem 300, referred to
herein as the Pathfinder system, under an embodiment. The Pathfinder system is
briefly described below, and is described in detail in the Related
Applications. Two
microphones Mic 1 and Mic 2 are used in the Pathfinder system, and Mic 1 is
considered the "signal" microphone. With reference to Figure 1, the Pathfinder
system 300 is equivalent to the NAVSAD system 100 when the voicing activity
detector (VAD) 320 is a non-acoustic voicing sensor 20 and the noise removal
subsystem 340 includes the detection subsystem 50 and the denoising subsystem
40. With reference to Figure 2, the Pathfinder system 300 is equivalent to the
PSAD system 200 in the absence of the VAD 320, and when the noise removal
subsystem 340 includes the detection subsystem 50 and the denoising subsystem
40.
The NAVSAD and PSAD systems support a two-level commercial approach
in which (i) a relatively less expensive PSAD system supports an acoustic
approach that functions in most low- to medium-noise environments, and (ii) a
NAVSAD system adds a non-acoustic sensor to enable detection of voiced speech
in any environment. Unvoiced speech is normally not detected using the sensor,
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as it normally does not sufficiently vibrate human tissue. However, in high
noise
situations detecting the unvoiced speech is not as important, as it is
normally very
low in energy and easily washed out by the noise. Therefore in high noise
environments the unvoiced speech is unlikely to affect the voiced speech
denoising. Unvoiced speech information is most important in the presence of
little
to no noise and, therefore, the unvoiced detection should be highly sensitive
in low
noise situations, and insensitive in high noise situations. This is not easily
accomplished, and comparable acoustic unvoiced detectors known in the art are
incapable of operating under these environmental constraints.
The NAVSAD and PSAD systems include an array algorithm for speech
detection that uses the difference in frequency content between two,
microphones
to calculate a relationship between the signals of the two microphones. This
is in
contrast to conventional arrays that attempt to use the time/phase difference
of
each microphone to remove the noise outside of an "area of sensitivity". The
methods described herein provide a significant advantage, as they do not
require a
specific orientation of the array with respect to the signal.
Further, the systems described herein are sensitive to noise of every type
and every orientation, unlike conventional arrays that depend on specific
noise
orientations. Consequently, the frequency-based arrays presented herein are
unique as they depend only on the relative orientation of the two microphones
themselves with no dependence on the orientation of the noise and signal with
respect to the microphones. This results in a robust signal processing system
with
respect to the type of noise, microphones, and orientation between the
noise/signal source and the microphones.
The systems described herein use the information derived from the
Pathfinder noise suppression system and/or a non-acoustic sensor described in
the Related Applications to determine the voicing state of an input signal, as
described in detail below. The voicing state includes silent, voiced, and
unvoiced
states. The NAVSAD system, for example, includes a non-acoustic sensor to
detect the vibration of human tissue associated with speech. The non-acoustic
sensor of an embodiment is a General Electromagnetic Movement Sensor
(GEMS) as described briefly below and in detail in the Related Applications,
but is
not so limited. Alternative embodiments, however, may use any sensor that is
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able to detect human tissue motion associated with speech and is unaffected by
environmental acoustic noise.
The GEMS is a radio frequency device (2.4 GHz) that allows the detection
of moving human tissue dielectric interfaces. The GEMS includes an RF
interferometer that uses homodyne mixing to detect small phase shifts
associated
with target motion. In essence, the sensor sends out weak elecfiromagnetic
waves
(less than 1 milliwatt) that reflect off of whatever is around the sensor. The
reflected waves are mixed with the original transmitted waves and the results
analyzed for any change in position of the targets. Anything that moves near
the
sensor will cause a change in phase of the reflected wave that will be
amplified
and displayed as a change in voltage output from the sensor. A similar sensor
is
described by Gregory C. Burnett (1999) in "The physiological basis of glottal
electromagnetic micropower sensors (GEMS) and their use in defining an
excitation function for the human vocal tract"; Ph.D. Thesis, University of
California
at Davis.
Figure 4 is a flow diagram of a detection algorithm 50 for use in detecting
voiced and unvoiced speech, under an embodiment. With reference to Figures 1
and 2, both the NAVSAD and PSAD systems of an embodiment include the
detection algorithm 50 as the detection subsystem 50. This detection algorithm
50
operates in real-time and, in an embodiment, operates on 20 millisecond
windows
and steps 10 milliseconds at a time, but is not so limited. The voice activity
determination is recorded for the first 10 milliseconds, and the second 10
milliseconds functions as a "look-ahead" buffer. While an embodiment uses the
20/10 windows, alternative embodiments may use numerous other combinations
of window values.
Consideration was given to a number of multi-dimensional factors in
developing the detection algorithm 50. The biggest consideration was to
maintaining the effectiveness of the Pathfiinder denoising technique,
described in
detail in the Related Applications and reviewed herein. Pathfinder performance
can be compromised if the adaptive filter training is conducted on speech
rather
than on noise. It is therefore important not to exclude any significant amount
of
speech from the VAD to keep such disturbances to a minimum.
Consideration was also given to the accuracy of the characterization
between voiced and unvoiced speech signals, and distinguishing each of these
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speech signals from noise signals. This type of characterization can be useful
in
such applications as speech recognition and speaker verification.
Furthermore, the systems using the detection algorithm of an embodiment
function in environments containing varying amounts of background acoustic
noise. If the non-acoustic sensor is available, this external noise is not a
problem
for voiced speech. However, for unvoiced speech (and voiced if the non-
acoustic
sensor is not available or has malfunctioned) reliance is placed on acoustic
data
alone to separate noise from unvoiced speech. An advantage inheres in the use
of two microphones in an embodiment of the Pathfinder noise suppression
system,
and the spatial relationship between the microphones is exploited to assist in
the
detection of unvoiced speech. However, there may occasionally be noise levels
high enough that the speech will be nearly undetectable and the acoustic-only
method will fail. In these situations, the non-acoustic sensor (or hereafter
just the
sensor) will be required to ensure good performance.
In the two-microphone system, the speech source should be relatively
louder in one designated microphone when compared to the other microphone.
Tests have shown that this requirement is easily met with conventional
microphones when the microphones are placed on the head, as any noise should
result in an H~ with a gain near unity.
Regarding the NAVSAD system, and with reference to Figure 1 and Fiigure
3, the NAVSAD relies on two parameters to detect voiced speech. These two
parameters include the energy of the sensor in the window of interest,
determined
in an embodiment by the standard deviation (SD), and optionally the cross-
correlation (XCORR) between the acoustic signal from microphone 1 and the
sensor data. The energy of the sensor can be determined in any one of a number
of ways, and the SD is just one convenient way to determine the energy.
For the sensor, the SD is akin to the energy of the signal, which normally
corresponds quite accurately to the voicing state, but may be susceptible to
movement noise (relative motion of the sensor with respect to the human user)
and/or electromagnetic noise. To further differentiate sensor noise from
tissue
motion, the XCORR can be used. The XCORR is only calculated to 15 delays,
which corresponds to just under 2 milliseconds at 5000 Hz.
The XCORR can also be useful when the sensor signal is distorted or
modulated in some fashion. For example, there are sensor locations (such as
the
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jaw or back of the neck) where speech production can be detected but where the
signal may have incorrect or distorted time-based information. That is, they
may
not have well defined features in time that will match with the acoustic
waveform.
However, XCORR is more susceptible to errors from acoustic noise, and in high
(<0 dB SNR) environments is almost useless. Therefore it should not be the
sole
source of voicing information.
The sensor detects human tissue motion associated with the closure of the
vocal folds, so the acoustic signal produced by the closure of the folds is
highly
correlated with the closures. Therefore, sensor data that correlates highly
with the
acoustic signal is declared as speech, and sensor data that does not correlate
well
is termed noise. The acoustic data is expected to lag behind the sensor data
by
about 0.1 to 0.8 milliseconds (or about 1-7 samples) as a result of the delay
time
due to the relatively slower speed of sound (around 330 m/s). However, an
embodiment uses a 15-sample correlation, as the acoustic wave shape varies
significantly depending on the sound produced, and a larger correlation width
is
needed to ensure detection.
The SD and XCORR signals are related, but are sufficiently different so that
the voiced speech detection is more reliable. For simplicity, though, either
parameter may be used. The values for the SD and XCORR are compared to
empirical thresholds, and if both are above their threshold, voiced speech is
declared. Example data is presented and described below.
Figures 5A, 5B, and 6 show data plots for an example in which a subject
twice speaks the phrase "pop pan", under an embodiment. Figure 5A plots the
received GEMS signal 502 for this utterance along with the mean correlation
504
between the GEMS signal and the Mic 1 signal and the threshold T1 used for
voiced speech detection. Figure 5B plots the received GEMS signal 502 for this
utterance along with the standard deviation 506 of the GEMS signal and the
threshold T2 used for voiced speech detection. Figure 6 plots voiced speech
602
detected from the acoustic or audio signal 608, along with the GEMS signal 604
and the acoustic noise 606; no unvoiced speech is detected in this example
because of the heavy background babble noise 606. The thresholds have been
r
set so that there are virtually no false negatives, and only occasional false
positives. A voiced speech activity detection accuracy of greater than 99% has
been attained under any acoustic background noise conditions.
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The NAVSAD can determine when voiced speech is occurring with high
degrees of accuracy due to the non-acoustic sensor data. However, the sensor
offers little assistance in separating unvoiced speech from noise, as unvoiced
speech normally causes no detectable signal in most non-acoustic sensors. If
there is a detectable signal, the NAVSAD can be used, although use of the SD
method is dictated as unvoiced speech is normally poorly correlated. In the
absence of a detectable signal use is made of the system and methods of the
Pathfinder noise removal algorithm in determining when unvoiced speech is
occurring. A brief review of the Pathfinder algorithm is described below,
while a
detailed description is provided in the Related Applications.
With reference to Figure 3, the acoustic information coming into
Microphone 1 is denoted by m1(n), the information~coming into Microphone 2 is
similarly labeled m2(n), and the GEMS sensor is assumed available to determine
voiced speech areas. In the z (digital frequency) domain, these signals are
represented as M1(z) and M2(z). Then
Ml ~z) = S(z)+ Nz (z)
Mz (z~ = N(z) + Sz (z)
with
Nz (z) = N(z~H~ (z~
Sz ~z~ = S~z~Hz ~z~
so that
Ml (z~ = S(z)+ N(z)Hl (z~ (1)
Mz ~z> = N(z)+ S(z~Hz (z)
This is the general case for all two microphone systems. There is always going
to
be some leakage of noise into Mic 1, and some leakage of signal into Mic 2.
Equation 1 has tour unknowns and only two relationships and cannot be solved
explicitly.
However, there is another way to solve for some of the unknowns in
Equation 1. Examine the case where the signal is not being generated - that
is,
where the GEMS signal indicates voicing is not occurring. In this case, s(n) =
S(z)
= 0, and Equation 1 reduces to
Ml" (z) = N(z)Hl (z)
Mz" (z~ = N(z)
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where the n subscript on the M variables indicate that only noise is being
received.
This leads to
Mln \Zl = M2n \ZIHl lZl
Hl ~z~ = Mm ~z~ (2)
M2n ~Z~
H~(z) can be calculated using any of the available system identification
algorithms
and the microphone outputs when only noise is being received. The calculation
can be done adaptively, so that if the noise changes significantly H~(z) can
be
recalculated quickly.
With a solution for one of the unknowns in Equation 1, solutions can be
found for another; H2(z), by using the amplitude of the GEMS or similar device
along with the amplitude of the two microphones. When the GEMS indicates
voicing, but the recent (less than 1 second) history of the microphones
indicate low
levels of noise, assume that n(s) = N(z) ~ 0. Then Equation 1 reduces to
M,S (z) = S(z)
Mzs ~z~ ='S~Z~H2 (z)
which in turn leads to
MzsO=MuO~HzO
Hz ~z~ = MZS ~z~
- Mn ~z~
which is the inverse of the H~(z) calculation, but note that different inputs
are being
used.
After calculating H~(z) and H2(z) above, they are used to remove the noise
from the signal. Rewrite Equation 1 as
S(2~ = Ml (z~ " N yHi ~z)
N(z) = MZ (z)- S(z~Hz (z)
,S(z> __ Ml (z) - CMa ~z~ - S~z)Hz ~z~~i ~z~
S(z~l - Hz (z)HI (z)~ = Mt ~z) - M2 (z)H, (z)
and solve for S(z) as:
S(z) = Mi ~z~ - Mz ~z~Hi ~z~ . (3.
1- Ha (z)HI ~z~
In practice H2(z) is usually quite small, so that HZ(z>Hl(z~ « l, and
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S~z) ~ Ml (z)- MZ (z~H, ~z),
obviating the need for the H2(z) calculation.
With reference to Figure 2 and Figure 3, the PSAD system is described.
As sound waves propagate, they normally lose energy as they travel due to
diffraction and dispersion. Assuming the sound waves originate from a point
source and radiate isotropically, their amplitude will decrease as a function
of 1/r,
where r is the distance from the originating point. This function of 1lr
proportional
to amplitude is the worst case, if confined to a smaller area the reduction
will be
less. However it is an adequate model for the configurations of interest,
specifically the propagation of noise and speech to microphones located
somewhere on the user's head.
Figure 7 is a microphone array for use under an embodiment of the PSAD
system. Placing the microphones Mic 1 and Mic 2 in a linear array with the
mouth
on the array midline, the difference in signal strength in Mic 1 and Mic 2
(assuming
the microphones have identical frequency responses) will be proportional to
both
d~ and ~d. Assuming a 1/r (or in this case 1/d) relationship, it is seen that
DM - ~Micll _ ~Hl (Z) ~ dl + ~d
IMic2l dl
where OM is the difference in gain between Mic 1 and Mic 2 and therefore
H~(z),
as above in Equation 2. The variable d~ is the distance from Mic 1 to the
speech
or noise source.
Figure 8 is a plot 800 of OM versus d~ for several 0d values, under an
embodiment. It is clear that as ~d becomes larger and the noise source is
closer,
OM becomes larger. The variable 0d will change depending on the orientation to
the speech/noise source, from the maximum value on the array midline to zero
perpendicular to the array midline. From the plot 800 it is clear that for
small dd
and for distances over approximately 30 centimeters (cm), DM is close to
unity.
Since most noise sources are farther away than 30 cm and are unlikely to be on
the midline on the array, it is probable that when calculating H~(z) as above
in
Equation 2, ~M (or equivalently the gain of H~(z)) will be close to unity.
Conversely, for noise sources that are close (within a few centimeters), there
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be a substantial difference in gain depending on which microphone is closer to
the
noise.
If the "noise" is the user speaking, and Mic 1 is closer to the mouth than Mic
2, the gain increases. Since environmental noise normally originates much
farther
away from the user's head than speech, noise will be found during the time
when
the gain of H~(z) is near unity or some fixed value, and speech can be found
after
a sharp rise in gain. The speech can be unvoiced or voiced, as long as it is
of
sufficient volume compared to the surrounding noise. The gain will stay
somewhat
high during the speech portions, then descend quickly after speech ceases. The
rapid increase and decrease in the gain of H~(z) should be sufficient to allow
the
detection of speech under almost any circumstances. The gain in this example
is
calculated by the sum of the absolute value of the filter coefficients. This
sum is
not equivalent to the gain, but the two are related in that a rise in the sum
of the
absolute value reflects a rise in the gain.
j
As an example of this behavior, Figure 9 shows a plot 900 of the gain
parameter 902 as the sum of the absolute values of H~(z) and the acoustic data
904 or audio from microphone 1. The speech signal was an utterance of the
phrase "pop pan", repeated twice. The evaluated bandwidth included the
frequency range from 2500 Hz to 3500 Hz, although 1500Hz to 2500 Hz was
additionally used in practice. Note the rapid increase in the gain when the
unvoiced speech is first encountered, then the rapid return to normal when the
speech ends. The large changes in gain that result from transitions between
noise
and speech can be detected by any standard signal processing techniques. The
standard deviation of the last few gain calculations is used, with thresholds
being
defined by a running average of the standard deviations and the standard
deviation noise floor. The later changes in gain for the voiced speech are
suppressed in this plot 900 for clarity.
Figure 10 is an alternative plot 1000 of acoustic data presented in Figure 9.
The data used to form plot 900 is presented again in this plot 1000, along
with
audio data 1004 and GEMS data 1006 without noise to make the unvoiced speech
apparent. The voiced signal 1002 has three possible values: 0 for noise, 1 for
unvoiced, and 2 for voiced. Denoising is only accomplished when V = 0. It is
clear
that the unvoiced speech is captured very well, aside from two single dropouts
in
the unvoiced detection near the end of each "pop". However, these single-
window
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dropouts are not common and do not significantly affect the denoising
algorithm.
They can easily be removed using standard smoothing techniques.
What is not clear from this plot 1000 is that the PSAD system functions as
an automatic backup to the NAVSAD. This is because the voiced speech (since it
has the same spatial relationship to the mics as the unvoiced) will be
detected as
unvoiced if the sensor or NAVSAD system fail for any reason. The voiced speech
will be misclassified as unvoiced, but the denoising will still not take
place,
preserving the quality of the speech signal.
However, this automatic backup of the NAVSAD system functions best in
an environment with low noise (approximately 10+ dB SNR), as high amounts (10
dB of SNR or less) of acoustic noise can quickly overwhelm any acoustic-only
unvoiced detector, including the PSAD. This is evident in the difference in
the
voiced signal data 602 and 1002 shown in plots 600 and 100 of Figures 6 and
10,
respectively, where the same utterance is spoken, but the data of plot 600
shows
no unvoiced speech because the unvoiced speech is undetectable. This is the
desired behavior when performing denoising, since if the unvoiced speech is
not
detectable then it will not significantly affect the denoising process. Using
the
Pathfinder system to detect unvoiced speech ensures detection of any unvoiced
speech loud enough to distort the denoising.
Regarding hardware considerations, and with reference to Figure 7, the
configuration of the microphones can have an effect on the change in gain
associated with speech and the thresholds needed to detect speech. In general,
each configuration will require testing to determine the proper thresholds,
but tests
with two very different microphone configurations showed the same thresholds
and
other parameters to work well. The first microphone set had the signal
microphone near the mouth and the noise microphone several centimeters away
at the ear, while the second configuration placed the noise and signal
microphones
back-to-back within a few centimeters of the mouth. The results presented
herein
were derived using the first microphone configuration, but the results using
the
other set are virtually identical, so the detection algorithm is relatively
robust with
respect to microphone placement.
A number of configurations are possible using the NAVSAD and PSAD
systems to detect voiced and unvoiced speech. One configuration uses the
NAVSAD system (non-acoustic only) to detect voiced speech along with the PSAD
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system to detect unvoiced speech; the PSAD also functions as a backup to the
NAVSAD system for detecting voiced speech. An alternative configuration uses
the NAVSAD system (non-acoustic correlated with acoustic) to detect voiced
speech along with the PSAD system to detect unvoiced speech; the PSAD also
functions as a backup to the NAVSAD system for detecting voiced speech.
Another alternative configuration uses the PSAD system to detect both voiced
and
unvoiced speech.
While the systems described above have been described with reference to
separating voiced and unvoiced speech from background acoustic noise, there
are
no reasons more complex classifications can not be made. For more in-depth
characterization of speech, the system can bandpass the information from Mic 1
and Mic 2 so that it is possible to see which bands in the Mic 1 data are more
heavily composed of noise and which are more weighted with speech. Using this
knowledge, it is possible to group the utterances by their spectral
characteristics
similar to conventional acoustic methods; this method would work better in
noisy
environments.
As an example, the "k" in "kick" has significant frequency content form 500
Hz to 4000 Hz, but a "sh" in "she" only contains significant energy from 1700-
4000
Hz. Voiced speech could be classified in a similar manner. For instance, an
/i/
("ee") has significant energy around 300 Hz and 2500 Hz, and an /a/ ('-'ah")
has
energy at around 900 Hz and 1200 Hz. This ability to discriminate unvoiced and
voiced speech in the presence of noise is, thus, very useful.
Each of the steps depicted in the flow diagrams presented herein can itself
include a sequence of operations that need not be described herein. Those
skilled
in the relevant art can create routines, algorithms, source code, microcode,
program logic arrays or otherwise implement the invention based on the flow
diagrams and the detailed description provided herein. The routines described
herein can be provided with one or more of the following, or one or more
combinations of the following: stored in non-volatile memory (not shown) that
forms part of an associated processor or processors, or implemented using
conventional programmed logic arrays or circuit elements, or stored in
removable
media such as disks, or downloaded from a server and stored locally at a
client, or
hardwired or preprogrammed in chips such as EEPROM semiconductor chips,
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application specific integrated circuits (ASICs), or by digital signal
processing
(DSP) integrated circuits.
Unless described otherwise herein, the information described herein is well
known or described in detail in the Related Applications. Indeed, much of the
detailed description provided herein is explicitly disclosed in the Related
Applications; most or all of the additional material of aspects of the
invention will
be recognized by those skilled in the relevant art as being inherent in the
detailed
description provided in such Related Applications, or well known to those
skilled in
the relevant art. Those skilled in the relevant art can implement aspects of
the
invention based on the material presented herein and the detailed description
provided in the Related Applications.
Unless the context clearly requires otherwise, throughout the description
and the claims, the words "comprise," "comprising," and the like are to be
construed in an inclusive sense as opposed to an exclusive or exhaustive
sense;
that is to say, in a sense of "including, but not limited to." Words using the
singular
or plural number also include the plural or singular number respectively.
Additionally, the words "herein," "hereunder," and words of similar import,
when
used in this application, shall refer to this application as a whole and not
to any
particular portions of this application.
The above description of illustrated embodiments of the invention is not
intended to be exhaustive or to limit the invention to the precise form
disclosed.
While specific embodiments of, and examples for, the invention are described
herein for illustrative purposes, various equivalent modifications are
possible within
the scope of the invention, as those skilled in the relevant art will
recognize. The
teachings of the invention provided herein can be applied to signal processing
systems, not only for the speech signal processing described above. Further,
the
elements and acts of the various embodiments described above can be combined
to provide further embodiments.
All of the above references and Related Applications are incorporated
herein by reference. Aspects of the invention can be modified, if necessary,
to
employ the systems, functions and concepts of the various references described
above to provide yet further embodiments of the invention.
These and other changes can be made to the invention in light of the above
detailed description. In general, in the following claims, the terms used
should not
14
CA 02448669 2003-11-26
WO 02/098169 PCT/US02/17251
be construed to limit the invention to the specific embodiments disclosed in
the
specification and the claims, but should be construed to include all speech
signal
systems that operate under the claims to provide a method for procurement.
Accordingly, the invention is not limited by the disclosure, but instead the
scope of
the invention is to be determined entirely by the claims.
While certain aspects of the invention are presented below in certain claim
forms, the inventor contemplates the various aspects of the invention in any
number of claim forms. Thus, the inventor reserves the right to add additional
claims after filing the application to pursue such additional claim forms for
other
aspects of the invention.
