*H. Strik, J. Jansen & L. Boves*

Proceedings ICSLP-92, Banff, Vol. 1, pp. 121-124.

ABSTRACT

Two methods are presented for automatic calculation of the voice source
parameters from continuous speech. Both methods are used to calculate the
voice source parameters for natural speech. However, for natural speech
no objective test procedure seems available. Therefore, both methods were
also tested on synthetic speech.

INTRODUCTION

Modern text-to-speech systems produce speech that is intelligible, but not
quite natural. A substantial improvement of the naturalness of synthetic
speech can probably be achieved by the use of a properly controlled voice
source model. To derive rules for voice source parameters large amounts
of data are required. Extracting voice source parameters by hand is time
consuming, subjective and thus probably not reproducible. Automatic extraction
of voice source parameters from continuous speech is also far from trivial.
Our aim is to develop methods for automatic extraction of voice source parameters
from continuous speech.In this article we propose and test two generic automatic
methods.

In continuous speech the glottal parameters may change from period to period.
Thus, the procedure must be pitch-synchronous. In our case, estimates of
the frequency response of the vocal tract are based on an analysis of the
closed glottis interval only. It is well known that analysis over such short
intervals can yield wildly fluctuating results if, for instance, the analysis
window is shifted or extended by just one or two samples. Because the causes
behind these fluctuations are not understood, it is not possible to determine
the optimal window location and length by simple automatic procedures. Therefore,
we estimate the vocal tract transfer function for a number of window positions
and lengths for each pitch period. The two methods differ in the way in
which these multiple estimates are used. Method 1 computes an inverse filtered
flow waveform for each individual estimate, and then computes the median
value for the parameters describing the glottal waveforms. Method 2 uses
the multiple estimates to obtain a single optimal estimate of the vocal
tract transfer function, that is subsequently used to obtain a single optimal
glottal flow waveform.

Both methods are used to derive voice source parameters from natural speech.
But for natural speech it is difficult to evaluate the performance objectively,
because the true glottal flow waveforms are not known. The only possible
evaluation is a subjective one, viz. checking that the voice source parameters
behave the way they are expected to behave. As long as there is no objective
test procedure for natural speech, an objective evaluation can only be done
with synthetic speech. In this article we will first test if both methods
give plausible results for natural speech. Then we will perform an objective,
quantitative test of both methods with synthetic speech.

MATERIAL

Natural speech

To study voice source characteristics data were obtained for four male subjects.
For the current article only the data of one subject were used. The speech
signal was transduced by a condensor microphone (B&K 4134) placed about
10 cm in front of the mouth, pre-amplified at the microphone (B&K 1619),
and amplified by a measuring amplifier (B&K 2607). The speech signal was
A/D converted off-line at a 10 kHz sampling rate.

Synthetic speech

The synthesis system that is used to generate the synthetic speech is a
serial pole-zero synthesizer [1] that uses the DEC-Talk source [2]. The
main reason for using the DEC-Talk source is its computational simplicity.
All synthesis signals have a sampling frequency of 10 kHz.

METHOD

Inverse filtering is often used to obtain an estimate of the glottal flow
signal. At the base of this method is the assumption that the voice source
and the vocal tract filter do not interact. It is known that this assumption
is not valid [3], but it is a useful approximation of the human speech system.
The approximation is best during the closed glottis intervals, because then
there is least interaction between the sub- and supraglottal cavities. Therefore,
the analysis window should preferably be confined to the closed glottis
interval. In [4] we showed that Closed glottis interval Covariance Linear
Predictive (CC-LP) analysis is as powerful as more sophisticated techniques,
like robust pole-zero analysis.

General method

A block diagram of the general method is shown in Fig. 1. First the vocal
tract filter is estimated. This is done by converting the results of the
CC-LP analysis to M Formant-Bandwidth pairs. This module is therefore called
"FB-EST" (see Fig. 1). The problem of finding the correct set of analysis
parameters is treated in the next section.

The estimated filter is inverted, and the inverse filter is used to filter
the audio signal (module IF in Fig. 1). The resulting inverse filtered signal,
INV, is a first estimate of the differentiated glottal volume flow. As the
inverse filtered signal often contains high-frequency noise, it is low-pass
filtered (module LPF in Fig. 1c). The resulting signal is a new, usually
better, estimate of the differentiated glottal volume flow, dUg. The glottal
volume flow, Ug, is obtained by integration of dUg (module INT in Fig. 1).

Voice source parameters, like open quotient and skewing, could be derived
directly from dUg and Ug. However, since these signals often are noisy direct
measurements yield unreliable values. Fitting a voice source model to the
data probably is a more robust method for obtaining voice source parameters.
In our system, the fit is done simultaneously on dUg and Ug [5].

The LF-model was used as voice source model because it seems useful for
synthesis, and because it has already been studied in great detail [6].
The model and its parameters are shown in Fig. 2. The LF-model is a four
parameter model. There are different combinations of the LF-parameters that
uniquely define a flow pulse. The four parameters that are used for the
generation of flow pulses during the fit procedure are U0, Tp, Te, and Ta.
Both during analysis and synthesis a fifth parameter is necessary to position
the LF-pulses, viz. T0.

The general method can be split in two parts. In the first part the formants
and bandwidths of the vocal tract filter are estimated (FB-EST), and in
the second part this filter is used to derive LF-parameters from the audio
signal. This second module is therefore called AUDIO2LF (see Fig. 1).

Analysis parameters

For CC-LP analysis a number of choices have to be made like position and
width of the closed glottis interval, order of the analysis, and pre-emphasis
factor. Usually, no combination of choices is optimal for the whole utterance.
However, for the natural speech that was used in this study a 12th order
LPC analysis with a pre-emphasis factor of 1.00 (+6 dB/oct) worked satisfactorily
for almost all pitch periods. Thus, window position and window length are
left as parameters that can be varied.

Generally, the moments of glottal closure are easier to detect than the
moments of opening. It is possible to identify the locations of the main
excitation from the audio signal [7]. However, we prefer to determine the
moments of glottal closure from the electroglottogram [5]. The signal with
the closure markers is called "MARKERS" (see Fig. 1). The window position
will be determined relative to a closure marker (this parameter is called
window shift). Five window shifts (of -2, -1, 0, 1, 2 samples) were used.
For synthetic speech the close markers are found by using the same operations
(differentiation, low-pass filtering, and peak-picking) for the source signal.

For natural speech it is difficult to obtain an accurate estimate of the
instant of glottal opening. Therefore, LP analysis is done for five different,
fixed window lengths. If the length of the window is too large, then the
last part will be in the open phase. This will perturb the estimates of
the formants and especially of the bandwidths. On the other hand, if the
length of the window is too short then LP anal-ysis will not be able to
make an accurate estimate. As a compromise we used window lengths of 33,
34, 35, 36, and 37 samples.

In both methods the FB-pairs are estimated for the 25 combinations of analysis
parameters (see Fig. 3). The 25 estimations of the vocal tract filter are
used by both methods to obtain a single set of voice source parameters.

Method 1

In the first method the module AUDIO2LF is used 25 times (see Fig. 3). The
result is 25 sets of LF-parameters. Finally median values are calculated
for the LF-parameters (module MEDIAN in Fig. 3), which results in one set
of average LF-parameters. This method is described in more detail in [5].

Method 2

In method 2 a Formant-Bandwidth TRacker (module FB-TR in Fig. 3) is used
to obtain an optimal set of FB-pairs. In this method the module AUDIO2LF
is applied once, where it uses the optimal filter to obtain one set of LF-parameters.

FB-Tracker

The goal of the module FB-TR is to find the first 4 FB-pairs. Generally,
the first 4 FB-pairs are among the first 5 poles that are modelled by the
LP analysis. Therefore, all 5 possible combinations of 4 out of 5 poles
are generated. This is done for the 25 estimates of the vocal tract filter,
and the result is a lattice of 4 FB-pairs with a depth of 125. A Viterbi
algorithm is used to find the optimal path in this lattice.

The optimal path is the path with minimal total cost. For calculation of
the cost, a transition and a local cost function is used. The transition
cost function is the Euclidean distance between the values of the current
frame and the values of the previous frame. The local cost function is the
Euclidean distance between the values of the current frame and reference
values. The reference values are obtained by a correlation LP analysis,
using an analysis window of 256 samples.

For higher formants the variation in both frequency and bandwidth values
is higher than for lower formants. For FB-tracking the effect would be that
the FB-values of higher formants are more important in determining the optimal
path. Therefore, all variables are converted to standard normal variables
by first subtracting the mean value, and subsequently dividing by the standard
deviation.

The total cost function has four contributions, viz. the transition and
local costs of the formants and the transition and local costs of the bandwidths.
Each of these four contributions can be given a weight. The weights that
have been used are 4, 2, 1, and 0, respectively. The reference bandwidths
that are obtained by correlation LP analysis are usually larger than the
bandwidths obtained by CC-LP. These bandwidths can not be compared in a
straightforward manner, and consequently the weight of the local cost of
the bandwidths is set to zero.

RESULTS

Natural speech

For the current article data are used that are obtained by applying both
methods to a natural utterance with a length of about 2.5 seconds. In Fig.
4 the audio signal of part of the utterance is shown, together with T0 and
the four LF-parameters as calculated by both methods.

The part of the utterance shown in Fig. 4 clearly demonstrates the dynamics
of the LF-parameters. Generally, it is observed that U0 covaries with the
amplitude of the speech signal. The same holds for the other amplitude related
parameters of the LF-model, like Ee and Ei. During transitions from vowels
to consonants it is often observed that U0 decreases, while the time parameters
T0, Te, Tp and Ta increase. The same effects were found by Bickley and Stevens
for artificial vocal tract constrictions [8].

The four LF-parameters and T0 can be used to calculate all other glottal
waveform parameters like open quotient, speed quotient, skewing etc. As
an example the 3 dimensionless wave shape parameters have been calculated:
Rg = T0/2Tp, Rk = Te/Tp - 1, and Ra = Ta/T0. The average values of Rg, Rk,
and Ra for all 194 voiced periods of the utterance are 111, 42, and 6.7
for method 1, and 110, 41, and 7.1 for method 2. These values are in accordance
with the values given in [9].

Although the results of both methods are slightly different, both methods
seem to give plausible results. Given these results, two questions emerged:
what is the reliability of the results, and which method is the best? As
there is no objective test procedure for natural speech, we also tested
both methods for synthetic speech.

Synthetic speech

The DEC-Talk waveform and the LF waveform are fundamentally different. LF-parameters
that are derived by both methods from synthetic speech can not be compared
directly to source signal that is used during synthesis. For evaluation
of the test results a reference was required. This reference was obtained
by performing the same operations on the source signal as on the inverse
filter results, i.e. low-pass filtering, integration and fitting of a LF-model.

The rationale behind this is that the differentiated source signal should
really be compared to the inverse filtered signals. All operations that
are necessary to obtain LF-parameters from the inverse filtered signals
should therefore also be applied to the differentiated source signal. In
Fig. 5 the low-pass filtered and the fitted signal are shown. Apart from
the fitted signal, the fit also yields the four LF-parameters for each pitch
period. These reference parameters are used below for evaluation. The utterance
used for evaluation was a random concatenation of all vowels, liquids and
glides that are used in the synthesis system.

In Fig. 5 it can be seen that low-pass filtering and fitting mainly influences
the excitation strength and the return phase. The effects are clear for
a synthetic glottal pulse (as in Fig. 5), but the same effects occur for
the inverse filtered signals that are derived from the speech signals. In
order to calculate the true values of the voice source parameters, a correction
is mandatory after both methods. The amount of both corrections can be calculated
from synthetic speech, and should be verified for natural speech. In the
following part we will only test the similarity of the reference parameters
(set 0) and the parameters obtained by both methods (set 1 and 2).

In Fig. 5 one can see that the reference signal and the source signals obtained
by both methods from the synthetic speech signal are very much alike. A
regression analysis on the voice source parameters has been done to test
the degree of resemblance. The results are given in Table I.

Table I. Results of regression analysis for different combinations of the
four LF-parameters. For each combination of two variables y and x a straight
line is fitted through the data: Y = intercept + slope*X. The correlation
coefficient R denotes the goodness of the fit (N = 232). In the top box
are given the comparisons of the results of both methods (subscript 1 and
2) with the reference (subscript 0), and in the bottom box the comparisons
between both methods.

Y X intercept slope R

U0,1 U0,0 -9.7 1.03 0.96

U0,2 U0,0 -11.4 1.05 0.97

Te,1 Te,0 -0.33 0.95 0.75

Te,2 Te,0 0.07 1.01 0.76

Tp,1 Tp,0 0.25 0.96 0.60

Tp,2 Tp,0 -0.09 1.05 0.62

Ta,1 Ta,0 0.05 0.95 0.69

Ta,2 Ta,0 0.00 1.00 0.64

U0,2 U0,1 3.8 0.99 0.98

Te,2 Te,1 -0.05 1.01 0.97

Tp,2 Tp,1 -0.09 1.02 0.96

Ta,2 Ta,1 0.02 0.90 0.80

The results of the comparisons between both methods and the reference are
given in the upper part of Table I. All slopes are about 1, and all intercepts
are small which means that the parameters calculated by both methods have,
on the average, the same value as the reference parameters. (The intercept
of U0 has a larger absolute value, but the value of the intercept relative
to the average value of U0 is even smaller than those of Te, Tp, and Ta.)
The correlation coefficients in Table I for U0 are almost 1, and those of
Te, Tp, and Ta are somewhat smaller but still highly significant. This means
that the values calculated by both methods for Te, Tp, and Ta closely resemble
the reference, while the calculated values for U0 and the reference values
are almost identical. Generally, the results of method 2 are slightly better
than those of method 1.

In the lower part of Table I both methods are compared. For U0, Te, and
Tp the intercept is small, and the correlation coefficient and the slope
are almost 1. This means that for U0, Te, and Tp the results of both methods
are very much alike. The resemblance of the values of Ta for both methods
is somewhat less.

CONCLUSIONS

In this article 2 methods are proposed for the automatic extraction of voice
source parameters from continuous speech. For natural speech both methods
produce comparable and reasonable results. There is a need for an objective
procedure to test the reliability of the extracted parameters. As long as
this test is not available, the best alternative is to test these methods
on synthetic speech.

The various operations that are used during the extraction of the voice
source parameters from speech, have influence on the magnitude of these
parameters. In order to re-estimate the true magnitude of the parameters
these effects have to be corrected.

From the tests on synthetic speech it appeared that both methods succeed
in estimating the voice source parameters quite accurately. The results
obtained for the amplitude parameter U0 are better than those of the time
parameters Te, Tp, and Ta. For synthetic speech the second method is slightly
better than the first method.

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