[0001] The invention relates to a method for estimating speech quality.
[0002] Providers of telecommunication network services have an interest in monitoring the
transmission quality of the telecommunication network as perceived by the end-user,
in particular with respect to the transmission of speech. For this so-called instrumental
or objective methods for speech quality estimation may be used that compare a reference
speech signal in form of an undistorted high-quality speech signal, which enters the
telecommunication network, with a test speech signal resulting from the reference
speech signal, the test speech signal being a speech signal to be tested and analysed,
respectively, after transmission via and/or processing by the telecommunication network
(including a simulation of a telecommunication network, i.e. a simulated telecommunication
network) and possible distortion, wherein the test speech signal is given by the reference
speech signal after transmission via and/or processing by the telecommunication network.
For comparing the reference speech signal and the test speech signal spectral representations
of the respective signals are usually used. The aim of the comparison of the reference
speech signal with the test speech signal is the determination of perceptually relevant
differences between the reference speech signal and the test speech signal.
[0003] The spectral representations of the reference speech signal and of the test speech
signal can be highly influenced by effects that have basically no or little disturbing
character for the perception of the end-user such as time differences, e.g. signal
delay, or differences in intensity (e.g. power, level or loudness) between the respective
speech signals. Usually, such differences are compensated by means of delay/time and
intensity alignment procedures before the actual differences between the spectral
representations of the reference speech signal and the test speech signal are computed.
Both the delay/time and the intensity alignment procedures are not restricted to the
compensation of a fixed bias, but can also be applied for time-varying compensation.
[0004] After the compensation of time/delay, intensity and possibly other undesired differences,
the remaining differences in the spectral representations of corresponding sections
of the reference speech signal and of the test speech signal are used to derive an
estimate of their similarity. Typically such similarity estimations are computed for
a number of short segments of the reference speech signal and the test speech signal.
The similarity estimations computed for the respective segments are then aggregated.
The aggregated similarity estimations represent a raw estimation of the overall speech
quality of the test signal (i.e. of the network transmission quality) with the raw
estimation usually being transformed to a common quality scale such as the known so-called
MOS scale (mean opinion score scale) ranging from 1 to 5.
[0006] Almost all methods for speech quality estimation that are commercially available
today follow the outlined approach that is schematically depicted in Figure 1. A reference
speech signal 1 enters e.g. by means of a mobile phone 2 a telecommunication network
3 resulting in a usually degraded test speech signal 4 received e.g. by a mobile phone
5 or tapped/scanned after receipt e.g. by the mobile phone 5, that shall be perceived
by an end-user 6. Box 7 illustrates the method for speech quality estimation. First
time/delay and intensity differences are compensated by alignment procedures (box
8). Then the spectral representations of the aligned speech signals 1, 4 are computed
and compared to give similarity estimations (box 9), wherein the computation and the
comparison is typically performed for short segments of the respective signals during
their entire duration (illustrated by arrow 10). From the similarity estimations the
speech quality of the test speech signal is estimated in box 11.
[0007] Corresponding known methods are described in
Beerends, J.G., Hekstra, A.P., Rix, A.W., Hollier, M.P., "Perceptual Evaluation of
Speech Quality (PESQ), the new ITU standard for end-to-end speech quality assessment.
Part I - Time alignment", J. Audio. Eng. Soc., Vol. 50, No. 10, October 2002,
Beerends, J.G., Hekstra, A.P., Rix, A.W., Hollier, M.P., "Peceptial Evaluation of
Speech Quality (PESQ), the new ITU standard for end-to-end speech quality assessment.
Part II - Psychoacoustic model", J. Audio Eng. Soc., Vol. 50, No. 10, October 2002,
Beerends, J.G., Stemerdink, J.A., "A perceptual speech quality measure based on a
psychoacoustic sound representation", J. Audio Eng. Soc., Vol. 42, No. 3, December
1994, and
ITU-T, Study Group 12, "TOSQA - Telecommunication objective speech quality assessment",
COM12-34-E, Geneva, December 1997.
[0008] The differences between the known methods lie mainly in the implementation details
of the steps corresponding to the boxes 8, 9 and 10 in Figure 1, as well as in the
way the spectral representations of the respective signals are transformed to a perceptual
representation of their frequency content. They also use different strategies for
post-processing and weighing of the raw estimations (i.e. the above-mentioned similarity
estimations). The aim of each known method is to achieve a high prediction accuracy
of the computed overall speech quality when compared to speech quality values obtained
in listening tests with human participants. Usually, methods for speech quality estimation
try to predict the mean opinion score (MOS) obtained in so-called absolute category
rating auditory experiments with human participants (
ITU-T Recommendation P.800, "Methods for subjective determination of transmission
quality", Geneva, 1996).
[0009] In order to estimate the speech quality of a test speech signal it is desirable to
determine which frequency bands in the test speech spectrum Y(f) have been amplified
or attenuated, respectively, when compared to the undistorted reference speech spectrum
X(f). According to a known, rather simple method the intensity difference between
the test speech spectrum Y(f) and the reference speech spectrum X(f) is determined
by calculating the difference D
1(f) = Y(f) - X(f) for each frequency band f. However, changes to the speech spectrum
intensity that are constant over all frequency bands, as e.g. caused by corresponding
signal attenuation properties of the transmission channel, usually contribute to the
perceived speech quality only to a limited extent. Rather, changes/modifications in
the relative intensity of the frequency bands within the speech spectrum have been
found to have a more significant effect on the perceived speech quality.
[0010] Figure 2 shows an example of a reference speech spectrum and a test speech spectrum,
wherein the test speech spectrum is uniformly attenuated over all frequency bands
when compared with the reference speech spectrum. The calculation of the difference
D
1(f) may yield a large absolute intensity difference despite the limited impairment
of the perceived speech quality. Figure 3 shows a further example of a reference speech
spectrum and a test speech spectrum, wherein the test speech spectrum Y(f) differs
from the reference speech spectrum X(f) only in a single frequency band f
i. For this example the calculation of the difference D
1(f) yields the desired measure of the perceived intensity difference, as the only
non-zero result for D
1(f) is obtained for the frequency band f being equal to f
i.
[0011] Alternatively, it is known to compute the intensity difference by using a scaled
test speech spectrum Y
2(f) = c·Y(f) with c being a scaling factor and given by c = average(X(f))/average(Y(f))
such that the average intensities of both spectra are aligned. The intensity difference
D
2(f) is then given by D
2(f) = Y
2(f) - X(f). For the example depicted in Figure 2 the computed intensity difference
D
2(f) is equal to zero for all frequency bands f, thus yielding a result that is closer
to the actually perceived speech quality impairment. For the example depicted in Figure
3 the calculation of D
2(f) however results in non-zero values for all frequency bands f, which does not correspond
to the perceived intensity difference as D
2(f) is not only non-zero for the frequency band f
i.
[0012] For time/delay compensation, known approaches are not just capable of computing the
overall time difference between the reference speech signal and the test speech signal
in time domain, but they can determine the time differences between individual parts
of the respective signals. For this corresponding parts of the reference speech signal
and of the test speech signal are matched. The signal parts are matched and their
respective time differences are typically computed in the order of the temporal/chronological
occurrence of the signal parts in the respective speech signals, i.e. signal parts
occurring at the end of the respective signals are matched after signal parts occurring
at the beginning of the respective speech signals have already been matched.
[0013] Figure 6 shows a corresponding example with a reference speech signal 201 and a test
speech signal 202 in time domain. The test speech signal 202 exhibits a positive time
difference, i.e. it starts later in time, when compared to the reference speech signal
201. The same applies to its signal parts when compared with corresponding signal
parts of the reference speech signal 201. The known matching procedure starts at the
beginning of the signals 201, 202 and progresses monotonously in time, yielding e.g.
the matched signal parts 203 and 204. However, signal parts of the reference speech
signal 201 following the match 204 can be matched with any signal part of the test
speech signal 202 that lies chronologically after the match 204. The already matched
signal parts 203 and 204 thus limit the number of possible matches by not taking into
account signal parts chronologically occurring before the signal part that is currently
matched. This approach can therefore lead to incorrect matching as illustrated by
the erroneous match 205. Such a matching procedure that starts at the beginning of
the speech signals 201 and 202 and progresses monotonously in time may only lead to
a limited extent to correct matching of later occurring signal parts. Consider in
this respect also the example of a test speech signal whose beginning has been muted.
A miscalculation in known approaches for time/delay compensation may lead to the non-muted
beginning of the reference speech signal being matched with an intact, non-muted part
of the test speech signal that shares some similarities with the beginning of the
reference speech signal that has been muted in the test speech signal, but occurs
chronologically later in the test speech signal. As a consequence, the part of the
reference speech signal, that actually corresponds to the aforementioned intact part
of the test speech signal that wrongly has been matched to the beginning of the reference
speech signal, can only be matched with signal parts of the test speech signal occurring
after this already matched intact part by means of the above-described known approach
(cf. Figure 6). This follows from the fact that each signal part can only be matched
once and from the matching being typically performed such that the temporal order
of matched signal parts in either signal is preserved. Therefore, the occurrence of
one incorrect matching may bias or deteriorate, respectively, the matching of signal
parts occurring chronologically later in the respective speech signals.
[0014] One of the typical problems of speech signal transmission is the interruption or
loss of speech. Known approaches rate the portions of the test speech signal with
missed speech by comparing the test speech signal with the reference speech signal
and measuring the amount of missed speech intensity, wherein the amount of missed
speech intensity can be computed from perceptual representations of the speech signals
such as loudness spectra. Hence, with known approaches the amount of missed speech
is related to the part of the reference speech signal that has actually been missed.
However, this approach might be disadvantageous as a human listener who listens to
the test speech signal does not rate missed speech in such manner. As the human listener
has no knowledge of the reference speech signal, he has no possibility to compare
the test speech signal with the reference speech signal, and he hence has no knowledge
of what is actually missing. As a consequence, the actually perceived distortion that
is caused by an interruption or loss of speech is rather related to the knowledge
and expectations of the human listener formed on the basis of the received portions
of the test speech signal.
[0015] It is an object of the invention to provide a method for estimating speech quality,
by which a speech quality estimate can be obtained which is close to the speech quality
perceived by an end-user (also referred to as human listener), in particular for the
examples of the reference speech spectrum and the test speech spectrum depicted in
Figures 2 and 3. It is a further object of the invention to provide a method for estimating
speech quality with improved time/delay compensation. It is a still further object
of the invention to provide a method for estimating speech quality, by which interruptions
or loss of speech, respectively, can be handled in a satisfactory manner. It is a
still further object of the invention to provide a method for estimating speech quality
that follows the basic approach depicted in and described with reference to Figure
1.
[0016] In order to implement these and still further objects of the invention, which will
become more readily apparent as the description proceeds, a method for estimating
speech quality is provided, wherein a reference speech signal enters a telecommunication
network, in particular a mobile network, resulting in a test speech signal, and that
comprises the steps of aligning the reference speech signal and the test speech signal
by matching signal parts of the reference speech signal with signal parts of the test
speech signal, wherein matched signal parts of the respective signals are of similar
length in time domain and have similar intensity summed over their length or relative
to their length, and of computing and comparing the speech spectra of the aligned
reference speech signal and the aligned test speech signal, the comparison resulting
in a difference measure that is indicative of the speech quality of the test speech
signal.
[0017] For matching signal parts of the reference speech signal with corresponding signal
parts of the test speech signal, first the one or more signal parts of the reference
speech signal that have the highest intensity summed over their length or relative
to their length, respectively, are matched with signal parts of the test speech signal.
Then the matching continues by matching signal parts of the reference speech signal
with signal parts of the test speech signal, whereby for the signal parts of the reference
speech signal to be matched the intensity summed over the length or relative to the
length, respectively, decreases for each subsequent match.
[0018] A performance measure is preferably computed for each pair of matched signal parts.
The performance measure is in particular given by the maximum of the cross-correlation
of the matched signal parts that is normalized by the signal powers of the matched
signal parts. If the performance measure of a pair of matched signal parts lies beneath
a pre-set threshold value, then the pair of matched signal parts is preferentially
deemed to have insufficient performance. Alternatively, a pair of matched signal parts
is deemed to have insufficient performance if its performance measure is significantly
lower than the performance measures of other pairs of matched signal parts. If a pair
of matched signal parts is deemed to have insufficient performance, then its signal
parts are preferably re-matched, i.e. matched again with other signal parts of the
respective signal. In particular, a pair of matched signal parts that is deemed to
have insufficient performance may be un-matched, i.e. the corresponding respective
signal parts may be made available again for matching with other signal parts that
have not yet been matched. Re-matching of the now again unmatched reference speech
signal part may be performed after further other reference speech signal parts have
been matched. Hence, employing the performance measure may result in a matching order
of the reference speech signal parts that differs from a matching order given by the
reference speech signal parts arranged in accordance to their respective intensity
summed over or relative to their respective length with decreasing intensity.
[0019] With the method according to the invention incorrect matching of signal parts of
the reference speech signal with signal parts of the test speech signal can advantageously
be reduced if not even avoided.
[0020] The method of the invention may also comprise the further steps of identifying a
number of perceptually dominant frequency sub-bands in one of the reference speech
spectrum and the test speech spectrum, with the reference speech signal having a reference
speech spectrum and the test speech signal having a test speech spectrum, computing
an intensity scaling factor for each identified sub-band by minimizing a measure of
the intensity difference between those parts of the reference speech spectrum and
the test speech spectrum that correspond to the respective sub-band, multiplying the
test speech spectrum with each intensity scaling factor thus generating a number of
scaled test speech spectra, selecting one scaled test speech spectrum, and computing
the difference between the selected scaled test speech spectrum and the reference
speech spectrum. This difference is indicative of the speech quality of the test speech
signal. The measure of the intensity difference is preferably given by the squared
intensity difference or the global maximum of the intensity difference between those
parts of the reference speech spectrum and of the test speech spectrum that correspond
to the respective sub-band.
[0021] The number of perceptually dominant sub-bands of one of the reference speech spectrum
and the test speech spectrum is preferably identified by determining the local maxima
in a perceptual representation of the respective spectrum and by selecting a predetermined
frequency range around each local maximum, wherein the predetermined frequency range
is preferentially determined by the local minima bordering the respective local maximum,
with one local minimum on each side (in frequency domain) of the respective local
maximum. In particular the predetermined frequency range shall be smaller or equal
to 4 Bark.
[0023] The selected scaled test speech spectrum is preferably given by the scaled test speech
spectrum yielding the lowest measure of the intensity difference between the reference
speech spectrum and a scaled test speech spectrum with the intensity difference being
computed for each scaled test speech spectrum. The measure of the intensity difference
is preferably given by the squared intensity difference or alternatively the global
maximum of the intensity difference between the reference speech spectrum and a respective
scaled test speech spectrum.
[0024] Through appropriate scaling of the test speech spectrum a difference between the
reference speech spectrum and the test speech spectrum can be computed that is close
to human perception, that in particular basically does not take into account amplifications
and attenuations, respectively, that are constant over all frequency bands, but that
places emphasis on modifications in the relative intensity of single frequency bands
that contribute to a qualitative impairment that would be perceived by a human listener.
[0025] The method of the invention may also comprise the further steps of for at least one
missed or interrupted signal part in the test speech signal computing the signal intensities
of the signal parts of the test speech signal that are adjacent to the missed or interrupted
signal part, deriving an expected signal intensity for the at least one missed or
interrupted signal part from the computed signal intensities of the adjacent signal
parts, computing a measure of the perceived distortion by comparing the actual intensity
of the at least one missed or interrupted signal part in the test speech signal with
the derived expected intensity for the at least one missed or interrupted signal part,
computing a measure of the actual distortion by comparing the reference speech signal
with the test speech signal, and combining the measure of the perceived distortion
with the measure of the actual distortion to generated a combined measure of distortion
that is indicative of the speech quality of the test speech signal.
[0026] The expected signal intensity of the at least one missed or interrupted signal part
in the test speech signal is preferably derived from the computed signal intensities
of the adjacent signal parts of the test speech signal by means of interpolation,
in particular by means of linear interpolation and/or spline interpolation.
[0027] By considering the signal intensities of signal parts of the test speech signal that
are adjacent to an interruption or speech loss it can be better assessed how such
a distortion is actually perceived. The method according to this still further aspect
of the invention is hence advantageously perceptually motivated.
[0028] The method of the invention can advantageously be combined with existing methods
for speech quality estimation that in particular have the structure depicted in and
described with respect to Figure 1 to improve and extend the existing methods.
[0029] Further advantageous features and applications of the invention can be found in the
dependent claims, as well as in the following description of the drawings illustrating
the invention. In the drawings like reference signs designate the same or similar
parts throughout the several figures of which:
Fig. 1 shows a block diagram illustrating the basic steps for speech quality estimation,
Fig. 2 shows a first example of a reference speech spectrum and of a test speech spectrum,
Fig. 3 shows a second example of a reference speech spectrum and of a test speech
spectrum,
Fig. 4 shows a flow chart of an embodiment of a method for estimating speech quality,
Fig. 5 shows a diagram illustrating the embodiment of the method for estimating speech
quality,
Fig. 6 shows a diagram illustrating a method for speech quality estimation according
to the state of the art,
Fig. 7 shows a diagram illustrating an embodiment of the method of the invention,
Fig. 8 shows a flow chart of a further embodiment of a method for estimating speech
quality, and
Fig. 9 shows a diagram illustrating a further embodiment of a method for estimating
speech quality.
Figures 1, 2, 3, and 6 have been described in the introductory part of the description
and reference is made thereto.
[0030] Figure 4 shows a flow chart of a first embodiment of a method for estimating speech
quality. In a first step 20 of this embodiment a certain number N of perceptually
dominant frequency sub-bands b
1...N is identified in and selected from one of the reference speech spectrum X(f) and
the test speech spectrum Y(f), for example from the undistorted reference speech spectrum
X(f), or from both spectra. The reference speech spectrum X(f) and the test speech
spectrum Y(f) represent exemplary speech spectra of a reference speech signal and
a test speech signal, respectively, which can both comprise several speech spectra.
The sub-bands bi are given by bi = [f
j...f
k], where f represents the frequency and where i, j, k, N are integers with k ≥ j and
i = 1...N. Using only perceptually dominant frequency sub-bands leads to the first
embodiment of the method for estimating speech quality being perceptually motivated.
[0031] For the identification of the perceptually dominant frequency sub-bands the respective
spectrum may be transformed to a perceptual representation of the respective spectrum,
the perceptual representation corresponding to the frequency content that is actually
received by a human auditory system. Then the local maxima of the perceptual representation
are identified and a predetermined range of frequencies around each local maximum
gives the perceptually dominant frequency sub-bands. The limiting values of each predetermined
range are preferentially given by the local minima adjacent to the respective local
maximum with the condition that the entire range is in particular smaller or equal
to 4 Bark. The loudness spectrum is one example of a perceptual representation that
has been found to represent to a high degree the human subjective response to auditory
stimuli.
[0032] In a second step 21 of this first embodiment of the method for estimating speech
quality, an intensity scaling factor c
i is preferably computed for each identified sub-band b
i. The respective intensity scaling factor c
i is computed such that the squared intensity difference |X(b
i)-ci·Y(b
i)|
2 between both spectra inside the respective identified sub-band bi is minimized.
[0033] In the following step 22 of this embodiment the test speech spectrum Y(f) is multiplied
with each intensity scaling factor c
i, thereby generating a number N of scaled test speech spectra given by Y
i(f) = c
i·Y(f).
[0034] Then in step 23 one scaled test speech spectrum Y
sel(f) of the generated scaled test speech spectra Y
i(f) is selected from the generated N scaled test speech spectra Y
i(f). The selection of the scaled test speech spectrum Y
sel(f) may be achieved by first computing the total squared intensity difference between
the reference speech spectrum X(f) and each of the generated N scaled test speech
spectra Y
i(f) over all frequency bands in the spectrum and by then selecting that particular
scaled test speech spectrum Y
sel(f) that yields the lowest total squared intensity difference such that the index
sel is given by sel = argmin
i(sum(|X(f) - Y
i(f)|)
2)).
[0035] Finally, in step 24 the difference between the selected scaled test speech spectrum
Y
sel(f) and the reference speech spectrum X(f) is computed in form of the spectral difference
function D(f) that is given by D(f) = Y
sel(f) - X(f). The spectral difference function D(f) contains non-zero values for frequency
bands that have been amplified and attenuated, respectively, when compared with the
reference speech spectrum X(f). Positive values of D(f) correspond to amplified spectrum
portions and negative values of D(f) correspond to attenuated spectrum portions. For
frequencies f lying inside the sub-band bi that corresponds to the scaling factor
c
i of the selected scaled test spectrum Y
sel(f), the spectral difference function D(f) normally contains small absolute values.
The spectral difference function D(f) constitutes an estimate of the speech quality.
[0036] In case of an amplification or attenuation of the reference test spectrum intensities
that is uniform over all frequency bands as depicted in Figure 2 for the case of an
attenuation, the computation of the spectral difference function D(f), that includes
the computation and selection of a scaled test speech spectrum, fully compensates
the difference between the reference speech spectrum X(f) and the test speech spectrum
Y(f) yielding a spectral difference function D(f) that is zero at all frequencies
f.
[0037] In the second example depicted in Figure 3 the test speech spectrum Y(f) differs
from the reference speech spectrum X(f) only in a single frequency band f
i. In this case the values of the computed spectral difference function D(f) depend
on whether the frequency band f
i is part of the selected sub-band b
sel, i.e. the sub-band bi whose intensity scaling factor c
i is the scaling factor of the selected scaled test speech spectrum Y
sel(f). If fi lies outside the selected sub-band b
sel, then the calculated scaling factor c is equal to 1 and the spectral difference function
D(f) has non-zero values only for f being equal to f
i. If however the frequency band f
i is part of the selected sub-band b
sel, then the value of the scaling factor c depends on the modified intensity at the
frequency f
i (modified in comparison to the reference speech intensity) and the selected scaled
test speech spectrum Y
sel(f) differs from the reference speech spectrum X(f) at frequencies other than f
i. The spectral difference function D(f) hence has a large number of non-zero values,
thereby reflecting the expected larger impact of a modification of intensities at
a frequency band that belongs to a perceptually dominant sub-band.
[0038] Hence, the first embodiment of the method for estimating speech quality computes
a difference that is indicative of the speech quality of the test speech signal in
form of the spectral difference function D(f) that provides better approximations
of the perceptions of speech spectrum intensity changes by a human listener when compared
with existing methods.
[0039] Figure 5 illustrates a possible application of the first embodiment of the method
for estimating speech quality. An example of a perceptual representation of a reference
speech spectrum 101 is shown along with an example of a perceptual representation
of a test speech spectrum 102. Compared to the perceptual representation of the reference
speech spectrum 101 the perceptual representation of the test speech spectrum 102
features an amplification of intensities at lower frequencies, as well as a limitation
of the bandwidth leading to a strong attenuation of the intensities at higher frequencies.
Without knowledge of the original, undistorted reference speech signal, the slight
amplification at lower frequencies is of rather limited influence on the perception
of speech quality by a human listener. However, the change in the relative intensities
of the various frequency bands (when compared to each other) within the perceptual
representation of the test speech spectrum 102, as well as the limitation of the bandwidth
have a much higher impact on the perceived speech quality.
[0040] For the example depicted in Figure 5 the perceptually dominant frequency sub-bands
are identified in the perceptual representation of the reference speech spectrum 101
as described above, i.e. by determining the local maxima and selecting a predetermined
frequency range around each local maximum. Highlighted area 104 corresponds to one
such perceptually dominant sub-band. Each identified perceptually dominant sub-band
gives rise to an intensity scaling factor and a correspondingly scaled test speech
spectrum. The dotted curve 103 in Figure 3 represents a perceptual representation
of a scaled test speech spectrum that has been scaled with the intensity scaling factor
associated with the sub-band 104.
[0041] Comparing the perceptual representation of the reference speech spectrum 101 with
the perceptual representation of the scaled test speech spectrum 103 it can be seen
from Figure 5 that the slight intensity difference at low frequencies between the
perceptual representation of the reference speech spectrum 101 and the perceptual
representation of the test speech spectrum 102 is strongly reduced and in this particular
case even equal to zero. The modification of the relative intensities of the frequency
bands within the perceptual representation of the test speech spectrum 102 when compared
with the perceptual representation of the reference speech spectrum 101 is still present
in the perceptual representation of the scaled test speech spectrum 103 and can be
measured as negative difference (i.e. attenuated spectrum portions) at middle frequencies
between the perceptual representation of the reference speech spectrum 101 and the
perceptual representation of the scaled test speech spectrum 103 (in Figure 5: basically
that portion of curve 103 that does not overlap with any other curve). Also the limitation
of the bandwidth is still present in the perceptual representation of the scaled test
speech spectrum 103, leading to a large negative difference at higher frequencies
when compared with the perceptual presentation of the reference speech spectrum 101.
This is in line with the change in the relative intensities of frequency bands and
with a limitation of the bandwidth at higher frequencies generally having a large
impact on the perceived speech quality.
[0042] As described in the introductory part of the description, with known approaches for
time/delay compensation corresponding signal parts of the reference speech signal
and the test speech signal are matched and their time difference is computed in the
order of the temporal/chronological occurrence of the respective signal parts in the
speech signals, starting at the beginning of the speech signals. This may, however,
result in erroneous matches for subsequent speech signal parts if a miscalculation
or erroneous match occurs at the beginning of the speech signals. This has been described
in detail above in connection with Figure 6.
[0043] An embodiment of the method of the invention avoids this disadvantage in that it
attempts to first match signal parts of the reference speech signal with corresponding
signal parts of the test speech signal, that are least likely to result in erroneous
matches. This is achieved by first starting to match the one or more parts of the
reference speech signal with the highest intensity summed over their length, e.g.
the parts of the reference speech signal with the highest signal energy or loudness
summed over their length. For the matching cross-correlation may be employed. Instead
of the highest intensity summed over the respective length the highest intensity relative
to the respective length may be used, and hence the highest signal energy or loudness
relative to the respective length. The parts of the reference speech signal identified
to have the highest intensity summed over their length (or relative to their length,
respectively), are matched according to the order of decreasing summed intensity (or
decreasing relative intensity, respectively). Degradations in the test speech signal
such as introduced e.g. by packet loss concealment routines in packetized transmission
systems often result in decreased signal energies of correspondingly degraded signal
parts in the test speech signal. High-energy signal parts of the reference signal
are therefore more likely to be still present with sufficiently high energy in the
test speech signal in comparison to low energy parts. The length of signal parts to
be matched can be in the range of 0.05 to 0.5 seconds.
[0044] After first matching signal parts of the reference speech signal with the highest
intensity summed over their length (or relative to their length, respectively), the
embodiment of the method of the invention attempts to match signal parts of the reference
signal with decreasing intensity summed over the length (or relative to their length,
respectively), i.e. it attempts to match signal parts in order of decreasing expected
match accuracy rather than monotonously progressing from the beginning of the reference
speech signal to the end of the reference speech signal. Such the possibility of erroneous
matching decreases with each further matched signal part of the reference speech signal,
since the remaining amount of matchable signal parts in the test speech signal is
limited by the amount of already matched signal parts, normally surrounding the signal
parts still to be matched in the time domain.
[0045] Before matching the signal parts of the reference speech signal, the reference speech
signal and the test speech signal are preferably pre-filtered by a bandpass filter
to filter out irrelevant signal parts such as background noise. The bandpass filter
is preferably configured such that it passes frequencies within the audio band, in
particular in the range of 700 Hz to 3000 Hz, and rejects or at least attenuates frequencies
outside the thus defined range.
[0046] The reference speech signal and the test speech signal are further preferably thresholded,
i.e. limited by a predefined threshold, and normalized with respect to their respective
signal energies/signal powers to compensate for differences between corresponding
signal parts of the reference speech signal and the test speech signal that are considered
irrelevant. Such irrelevant differences may, for example, be caused by varying gain
properties of the transmission channel/telecommunication network in question. The
computational operations that are performed for the thresholding and normalization
are preferably configured and performed such that a sliding window of preferably 26.625
ms length is moved over the entire length of both speech signals in time domain, that
for each speech signal the average signal power within the sliding window is computed
while the sliding window is moved over the respective speech signal, and that the
average signal power within each sliding window is re-scaled to either a first predefined
value if it exceeds a pre-set threshold value, or otherwise is set to a second predefined
value. This pre-set threshold value is preferentially set equal to (S + 3*N)/4 with
S being the average signal level of the speech content within the respective speech
signal and N being the signal level of the background noise in the respective speech
signal. The value for S may, for example, be computed as described in
ITU-T Recommendation P.56 "Objective measurement of active speech level", Geneva,
1993. The second predefined value is chosen smaller than the first predefined value. The
second predefined value may e.g. be equal to 0.
[0047] For computing the intensity summed over the length of signal parts of the respective
speech signals, the respective intensities are preferably compared with a second pre-set
threshold value and only those intensities are taken into account and summed up that
exceed this second pre-set threshold value. The second pre-set threshold value lies
preferentially slightly above the above-mentioned first threshold value (S + 3*N)/4.
The second pre-set threshold value is preferably given by 0.4*S + 0.6*N with S and
N as defined in the last paragraph.
[0048] Figure 7 shows a diagram illustrating the embodiment of the method of the invention.
In the diagram a reference speech signal 301 and a test speech signal 302 are shown
in the time domain. By first matching those signal parts of the reference speech signal
301 with corresponding signal parts of the test speech signal 302 that are least likely
to result in erroneous matches, i.e. by first matching those signal parts with the
highest intensity summed over their length (or relative to their length, respectively),
the speech signals 301 and 302 are subdivided into smaller sections. In Figure 7 these
sections are given by the respective speech signals 301 and 302 without the already
matched signal parts 303 and 304. Signal parts within the remaining sections of the
reference speech signal 301 can only be matched with signal parts of the test speech
signal 302 that occur in the corresponding section of the test speech signal 302,
with the temporal locations of the already matched signal parts surrounding or limiting,
respectively, the sections.
[0049] In the example shown in Figure 7 the signal part of the reference speech signal 301
between the matches 303 and 304 can only be matched with a corresponding signal part
of the test speech signal 302, i.e. with a signal part of the test speech signal 302
that lies between the signal parts of the test speech signal 302 of the matches 303
and 304 in the time domain. The embodiment of the method of the invention thus reduces
the possibility of incorrect matching by subdividing the reference speech signal and
the test speech signal into smaller sections, the sections being separated by already
performed matches.
[0050] Preferably a performance measure (also called performance metric) is computed for
each matched pair 303, 304 of signal parts. The performance measure may for example
be given by the maximum of the waveform cross-correlation of the matched signal parts
of the reference speech signal and the test speech signal, the waveform cross-correlation
being normalized by the signal powers of the respective signal parts. A decision unit
may be provided to assess the performance of each pair of matched signal parts by
evaluating their associated performance measure. The decision unit evaluates if the
performance measure is equal to or exceeds a pre-set threshold value. If the value
of the performance measure is neither equal to nor exceeds the pre-set threshold value
then the decision unit interprets this finding as the pair of matched signal parts
having insufficient performance, i.e. as the match being poor.
[0051] The decision unit may also compare the performance measure for a particular pair
of matched signal parts with the performance measures computed for other pairs of
matched signal parts or with the average value of the performance measures computed
for other pairs of matched signal parts, respectively. If the performance measure
of the particular pair of matched signals is significantly lower than the performance
measures (or the average of the performance measures) of the other pairs of matched
signal parts, i.e. if the difference between the performance measure of the particular
pair of matched signals and the performance measures (or the average of the performance
measures) of the other pairs of matched signal parts exceeds a pre-defined threshold
value, then the decision unit may assess the particular pair of matched signal parts
as having insufficient performance.
[0052] If a pair of matched signal parts is assessed as having insufficient performance,
then the decision unit may reject the particular pair of matched signal parts and
skip those signal parts, so that the signal parts may be used for later matching,
i.e. may be re-matched later. The matching is then preferably first continued for
different signal parts, thus subdividing the reference speech signal and the test
speech signal into smaller sections. Matching of the skipped signal parts is then
preferably reattempted when the possibility of erroneous matching has been further
reduced by further subdivision of the reference speech signal and the test speech
signal, or when no other unmatched signal parts of the reference signal are left for
matching.
[0053] When transmitting speech signals via a telecommunication network distortions may
occur due to interruptions of the speech signal or missed speech (missed parts of
the speech signal) caused for example by a temporary dead spot within the telecommunication
network. Common approaches calculate the amount of distortion caused by such an interruption
or loss of speech based on the signal intensity (e.g. the power, level or loudness)
that is missing or decreased in the test speech signal when compared to the corresponding
signal part(s) in the reference speech signal. However, these common approaches do
not take into account that a human listener who listens to the test speech signal
has no knowledge of the reference speech signal as such and thus does not know how
much signal intensity is actually missing.
[0054] According to a second embodiment of a method for estimating speech quality the test
speech signal is analysed shortly before and shortly after the location of an occurrence
of an interruption or a speech loss, i.e. the analysing takes place at instances (i.e.
signal parts) in the test speech signal that are known to a human listener. It is
expected that low signal intensities (e.g. power, level or loudness) in these signal
parts of the test speech signal lead to a relatively low perceived distortion for
a human listener. Even though the actually missed or interrupted signal part may be
of higher signal intensity than the remaining surrounding signal parts, it is assumed
that a human listener does not perceive the interruption or speech loss as strong
since he does not expect the signal intensity to be high, his expectation being based
on the lower signal intensity of the surrounding signal parts in the test speech signal.
[0055] Figure 9 depicts an example of a reference speech signal 401 and a test speech signal
402 in the time domain, wherein a signal part 403 with high signal intensity is lost
during transmission and thus missing in the test speech signal 402. The corresponding
signal part 404 in the test speech signal has in comparison extremely low signal intensity.
[0056] Figure 8 depicts a flow chart of the second embodiment of the method for estimating
speech quality. In a first step 30 of the second embodiment of the method for estimating
speech quality the signal intensities of the signal parts of the test speech signal
are computed that lie adjacent to the missed or interrupted signal part, i.e. that
surround the interruption or speech loss. In a next step 31 the expected signal intensity
at the location of the interruption or speech loss in the test speech signal is computed.
This expected signal intensity is derived from the computed signal intensities of
the adjacent signal parts that have been computed in step 30. The expected signal
intensity at the interruption or speech loss may be derived from the computed signal
intensities of the adjacent signal parts by means of interpolation, in particular
by means of linear and/or spline interpolation.
[0057] In a next step 32 a measure of the perceived distortion is computed by comparing
a test speech signal, in which the interruption or loss has been replaced by the derived
expected signal intensity of the missed or interrupted signal part, with the actual
test speech signal. In particular the measure of the perceived distortion is computed
by comparing the actual intensity of the at least one missed or interrupted signal
part in the test speech signal with the derived expected intensity for the at least
one missed or interrupted signal part. The computed measure of the perceived distortion
lies preferable in the range of 0 to 1. In step 33 a measure of the actual distortion
is computed by comparing the reference speech signal with the actual test speech signal.
The order of steps 32 and 33 can be interchanged. Steps 32 and 33 may also be performed
concurrently.
[0058] Finally, in step 34 the computed measure of the perceived distortion is combined
with the computed measure of the actual distortion yielding a combined measure of
distortion that may be used to assess the speech quality impairment caused by the
interruption or speech loss. For combining the measure of the perceived distortion
with the measure of the actual distortion, the measure of the perceived distortion
may be multiplied with the measure of the actual distortion to compute the combined
measure of distortion. Additionally or alternatively, the combined measure of distortion
may be given by the measure of the actual distortion limited to the measure of the
perceived distortion if the measure of the actual distortion exceeds the measure of
the perceived distortion. Still additionally or alternatively, the combined measure
of distortion may be given by the measure of the actual distortion exponentially weighted
by the measure of the perceived distortion, i.e. by an exponentiation with the measure
of the actual distortion being the base and the measure of the perceived distortion
being the exponent. Still additionally or alternatively, the combined measure of distortion
may be given by the difference (computed through subtraction) between the measure
of the perceived distortion and the measure of the actual distortion. The above-mentioned
ways for computing the combined measure of distortion may be combined in any perceivable
ways.
[0059] Through the computation of expected signal intensities of missed or interrupted signal
parts from signal intensities of adjacent signal parts in the test speech signal,
it can be better assessed by the second embodiment of the method for estimating speech
quality how such a distortion is actually perceived by a human listener.
[0060] The first and the second embodiments of the method for estimating speech quality
and the embodiment of the method of the invention may be combined in various combinations,
i.e. the first embodiment of the method for estimating speech quality may be combined
with the second embodiment of the method for estimating speech quality and/or the
embodiment of the method of the invention, the embodiment of the invention may be
combined with the first and/or the second embodiment of the method for estimating
speech quality, and the second embodiment of the method for estimating speech quality
may be combined with the first embodiment for estimating speech quality and/or the
embodiment of the method of the invention.
1. A method for estimating speech quality,
wherein a reference speech signal (301) enters a telecommunication network resulting
in a test speech signal (302), the method comprising the following steps:
- aligning the reference speech signal (301) and the test speech signal (302) by matching
signal parts of the reference speech signal (301) with signal parts of the test speech
signal (302), wherein matched signal parts (303, 304) are of similar length in the
time domain and have similar intensity summed over their length, and
- computing and comparing the speech spectra of the reference speech signal (301)
and the test speech signal (302) that are aligned, resulting in a difference measure,
the difference measure being indicative of the speech quality of the test speech signal,
wherein for the matching of signal parts of the reference speech signal (301) with
signal parts of the test speech signal (302), first the one or more signal parts of
the reference speech signal (301) with the highest intensity summed over their length
are matched with corresponding signal parts of the test speech signal (302), then
the matching continues with signal parts of the reference speech signal (301) with
decreasing intensity summed over their length.
2. The method according to claim 1, wherein the reference speech signal (301) and the
test speech signal (302) are each pre-filtered by a bandpass filter, in particular
by a bandpass filter with a frequency range that corresponds to the audio band.
3. The method according to claim 1 or 2,
wherein a performance measure is computed for each pair of matched signal parts (303,
304), the performance measure being in particular the maximum of the cross-correlation
of the matched signal parts (303, 304) normalized by the signal powers of the matched
signal parts (303, 304).
4. The method according to claim 3, wherein a pair of matched signal parts is deemed
to have insufficient performance if its performance measure lies beneath a pre-set
threshold value.
5. The method according to claim 3, wherein a pair of matched signal parts is deemed
to have insufficient performance if its performance measure is significantly lower
than the performance measures of other pairs of matched signal parts (303, 304).
6. The method according to claim 4 or 5,
wherein each signal part of a pair of matched signal parts with deemed insufficient
performance is re-matched.
7. The method according to one of the preceding claims, wherein the reference speech
signal (101) has a reference speech spectrum (X) and the test speech signal (102)
has a test speech spectrum (Y) and wherein the method further comprises the following
steps:
- identifying a number of perceptually dominant frequency sub-bands (bi) in one of
the reference speech spectrum (X) and the test speech spectrum (Y),
- computing an intensity scaling factor (ci) for each identified sub-band (bi) by minimizing a measure of the intensity difference
between those parts of the reference speech spectrum (X) and of the test speech spectrum
(Y) that correspond to the respective sub-band (bi).
- multiplying the test speech spectrum (Y) with each intensity scaling factor (ci) thereby generating a number of scaled test speech spectra (Yi),
- selecting one scaled test speech spectrum,
and
- computing the difference between the selected scaled test speech spectrum (Ysel) and the reference speech spectrum (X), the difference being indicative of the speech
quality of the test speech signal (102).
8. The method according to claim 7, wherein the measure of the intensity difference is
given by the squared intensity difference or the global maximum of the intensity difference
between those parts of the reference speech spectrum (X) and of the test speech spectrum
(Y) that correspond to the respective sub-band (bi).
9. The method according to claim 7 or 8,
wherein the number of perceptually dominant sub-bands (bi) of one of the reference
speech spectrum (X) and the test signal spectrum (Y) is identified by determining
the local maxima in a perceptual representation of the respective spectrum and by
selecting a predetermined range of frequencies around each local maximum.
10. The method according to claim 9, wherein the predetermined range of frequencies is
determined by the local minima bordering the respective local maximum with the predetermined
range of frequencies being in particular smaller or equal to 4 Bark.
11. The method according to claim 9 or 10,
wherein the perceptual representation of the respective spectrum is obtained by transforming
the respective spectrum to a loudness spectrum.
12. The method according to one of the claims 7 to 11, wherein a measure of the intensity
difference between the reference speech spectrum (X) and a respective scaled test
speech spectrum (Yi) is computed for each scaled test speech spectrum (Yi) and wherein the scaled test speech spectrum is selected that yields the lowest measure
of the intensity difference.
13. The method according to claim 12, wherein the measure of the intensity difference
is given by the squared intensity difference or the global maximum of the intensity
difference between the reference speech spectrum (X) and a respective scaled test
speech spectrum (Yi).
14. A method according to one of the preceding claims, wherein the method further comprises
the following steps:
- for at least one missed or interrupted signal part in the test speech signal (402)
computing the signal intensities of the signal parts adjacent to the missed or interrupted
signal part,
- deriving an expected signal intensity for the at least one missed or interrupted
signal part from the computed signal intensities of the adjacent signal parts of the
test speech signal (402),
- computing a measure of the perceived distortion by comparing the actual intensity
of the at least one missed or interrupted signal part in the test speech signal (402)
with the derived expected intensity for the at least one missed or interrupted signal
part,
- computing a measure of the actual distortion by comparing the reference speech signal
(401) with the test speech signal (402), and
- combining the measure of the perceived distortion with the measure of the actual
distortion to generate a combined measure of distortion indicative of the speech quality
of the test speech signal (402).
15. The method according to claim 14, wherein the expected signal intensity of the at
least one missed or interrupted signal part is derived from the computed signal intensities
of the adjacent signal parts of the test speech signal (402) by means of interpolation,
in particular by means of linear interpolation and/or spline interpolation.