AU2020227065A1 - Method for forming the excitation signal for a glottal pulse model based parametric speech synthesis system - Google Patents

Abstract

A method is presented for forming the excitation signal for a glottal pulse model based parametric speech synthesis system. In one embodiment, fundamental frequency values are used to form the excitation signal. The excitation is modeled using a voice source pulse selected from a database of a given speaker. The voice source signal is segmented into glottal segments, which are used in vector representation to identify the glottal pulse used for formation of the excitation signal. Use of a novel distance metric and preserving the original signals extracted from the speakers voice samples helps capture low frequency information of the excitation signal. In addition, segment edge artifacts are removed by applying a unique segment joining method to improve the quality of synthetic speech while creating a true representation of the voice quality of a speaker.

Description

TITLE METHOD FOR FORMING THE EXCITATION SIGNAL FOR A GLOTTAL PULSE MODEL BASED PARAMETRIC SPEECH SYNTHESIS SYSTEM BACKGROUND

[0001] The present invention generally relates to telecommunications systems and methods, as well as

speech synthesis. More particularly, the present invention pertains to the formation of the excitation

signal in a Hidden Markov Model based statistical parametric speech synthesis system.

SUMMARY

[0002] A method is presented for forming the excitation signal for a glottal pulse model based

parametric speech synthesis system. In one embodiment, fundamental frequency values are used to

form the excitation signal. The excitation is modeled using a voice source pulse selected from a

database of a given speaker. The voice source signal is segmented into glottal segments, which are

used in vector representation to identify the glottal pulse used for formation of the excitation signal.

Use of a novel distance metric and preserving the original signals extracted from the speakers voice

samples helps capture low frequency information of the excitation signal. In addition, segment edge

artifacts are removed by applying a unique segment joining method to improve the quality of synthetic

speech while creating a true representation of the voice quality of a speaker.

[0003] In one embodiment, a method is presented to create a glottal pulse database from a speech

signal, comprising the steps of: performing pre-filtering on the speech signal to obtain a pre-filtered

signal; analyzing the pre-filtered signal to obtain inverse filtering parameters; performing inverse

filtering of the speech signal using the inverse filtering parameters; computing an integrated linear

prediction residual signal using the inversely filtered speech signal; identifying glottal segment

boundaries in the speech signal; segmenting the integrated linear prediction residual signal into glottal pulses using the identified glottal segment boundaries from the speech signal; performing normalization of the glottal pulses; and forming the glottal pulse database by collecting all normalized glottal pulses obtained for the speech signal.

[0004] In another embodiment, a method is presented to form parametric models, comprising the

steps of: computing a glottal pulse distance metric between a number of glottal pulses; clustering the

glottal pulse database into a number of clusters to determine centroid glottal pulses; forming a

corresponding vector database by associating a vector with each glottal pulse in the glottal pulse

database, wherein the centroid glottal pulses and the distance metric is defined mathematically to

determine association; determining Eigenvectors of the vector database; and forming parametric

models by associating a glottal pulse from the glottal pulse database to each determined Eigenvector.

[0005] In yet another embodiment, a method is presented to synthesize speech using input text,

comprising the steps of: a) converting the input text into context dependent phone labels; b) processing

the phone labels created in step (a) using trained parametric models to predict fundamental frequency

values, duration of the speech synthesized, and spectral features of the phone labels; c) creating an

excitation signal using an Eigen glottal pulse and said predicted one or more of: fundamental frequency

values, spectral features of phone labels, and duration of the speech synthesized; and d) combining the

excitation signal with the spectral features of the phone labels using a filter to create synthetic speech

output.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 is a diagram illustrating an embodiment of an Hidden Markov Model based Text to

Speech system.

[0007] Figure 2 is a diagram illustrating an embodiment of a signal.

[0008] Figure 3 is a diagram illustrating an embodiment of excitation signal creation.

[0009] Figure 4 is a diagram illustrating an embodiment of excitation signal creation.

[0010] Figure 5 is a diagram illustrating an embodiment of overlap boundaries.

[0011] Figure 6 is a diagram illustrating an embodiment of excitation signal creation.

[0012] Figure 7 is a diagram illustrating an embodiment of glottal pulse identification.

[0013] Figure 8 is a diagram illustrating an embodiment of glottal pulse database creation.

DETAILED DESCRIPTION

[0014] For the purposes of promoting an understanding of the principles of the invention, reference

will now be made to the embodiment illustrated in the drawings and specific language will be used to

describe the same. It will nevertheless be understood that no limitation of the scope of the invention is

thereby intended. Any alterations and further modifications in the described embodiments, and any

further applications of the principles of the invention as described herein are contemplated as would

normally occur to one skilled in the art to which the invention relates.

[0015] Excitation is generally assumed to be a quasi-periodic sequence of impulses for voiced regions.

Each sequence is separated from the previous sequence by some duration, such as To = , where F0

' To represents pitch period and F represents fundamental frequency. The excitation, in unvoiced

regions, is modeled as white noise. In voiced regions, the excitation is not actually impulse sequences.

The excitation is instead a sequence of voice source pulses which occur due to vibration of the vocal

folds. The pulses' shapes may vary depending on various factors such as the speaker, the mood of the

speaker, the linguistic context, emotions, etc.

[0016] Source pulses have been treated mathematically as vectors by length normalization (through

resampling) and impulse alignment, as described in European Patent EP 2242045 (granted June 27,

2012, inventors Thomas Drugman, et al.) The final length of normalized source pulse signal is resampled

to meet the target pitch. The source pulse is not chosen from a database, but obtained over a series of

calculations which compromise the pulse characteristics in the frequency domain. In addition, the

approximate excitation signal used for creating a pulse database does not capture low frequency source content as there is no pre-filtering done while determining the Linear Prediction (LP) coefficients, which are used for inverse filtering.

[0017] In statistical parametric speech synthesis, speech unit signals are represented by a set of

parameters which can be used to synthesize speech. The parameters may be learned by statistical

models, such as HMMs, for example. In an embodiment, speech may be represented as a source-filter

model, wherein source/excitation is a signal which when passed through an appropriate filter produces

a given sound. Figure 1 is a diagram illustrating an embodiment of a Hidden Markov Model (HMM)

based Text to Speech (TTS) system. An embodiment of an exemplary system may contain two phases,

for example, the training phase and the synthesis phase.

[0018] The Speech Database 105 may contain an amount of speech data for use in speech synthesis.

During the training phase, a speech signal 106 is converted into parameters. The parameters may be

comprised of excitation parameters and spectral parameters. Excitation Parameter Extraction 110 and

Spectral Parameter Extraction 115 occurs from the speech signal 106 which travels from the Speech

Database 105. A Hidden Markov Model 120 may be trained using these extracted parameters and the

Labels 107 from the Speech Database 105. Any number of HMM models may result from the training

and these context dependent HMMs are stored in a database 125.

[0019] The synthesis phase begins as the context dependent HMMs 125 are used to generate

parameters 140. The parameter generation 140 may utilize input from a corpus of text 130 from which

speech is to be synthesized from. The text 130 may undergo analysis 135 and the extracted labels 136

are used in the generation of parameters 140. In one embodiment, excitation and spectral parameters

may be generated in 140.

[0020] The excitation parameters may be used to generate the excitation signal 145, which is input,

along with the spectral parameters, into a synthesis filter 150. Filter parameters are generally Mel

frequency cepstral coefficients (MFCC) and are often modeled by a statistical time series by using

HMMs. The predicted values of the filter and the fundamental frequency as time series values maybe

used to synthesize the filter by creating an excitation signal from the fundamental frequency values and

the MFCC values used to form the filter.

[0021] Synthesized speech 155 is produced when the excitation signal passes through the filter. The

formation of the excitation signal 145 is integral to the quality of the output, or synthesized, speech 155.

Low frequency information of the excitation is not captured. It will thus be appreciated that an

approach is needed to capture the low frequency source content of the excitation signal and to improve

the quality of synthetic speech.

[0022] Figure 2 is a graphical illustration of an embodiment of the signal regions of a speech segment,

indicated generally at 200. The signal has been broken down into segments based on fundamental

frequency values for categories such as voiced, unvoiced, and pause segments. The vertical axis 205

illustrates fundamental frequency in Hertz (Hz) while the horizontal axis 210 represents the passage of

milliseconds (ms). The time series, FO, 215 represents the fundamental frequency. The voiced region,

220 can be seen as a series of peaks and may be referred to as a non-zero segment. The non-zero

segments 220 may be concatenated to form an excitation signal for the entire speech, as described in

further detail below. The unvoiced region 225 is seen as having no peaks in the graphical illustration 200

and may be referred to as zero segments. The zero segments may represent a pause or an unvoiced

segment given by the phone labels.

[0023] Figure 3 is a diagram illustrating an embodiment of excitation signal creation indicated generally

at 300. Figure 3 illustrates the creation of the excitation signal for both unvoiced and pause segments.

The fundamental frequency time series values, represented as FO, represent signal regions 305 that are

broken down into voiced, unvoiced, and pause segments based on the FO values.

[0024] An excitation signal 320 is created for unvoiced and pause segments. Where pauses occur,

zeroes (0) are placed in the excitation signal. In unvoiced regions, white noise of appropriate energy (in

one embodiment, this may be determined empirically by listening tests) is used as the excitation signal.

[0025] The signal regions, 305, along with the Glottal Pulse 310 are used for excitation generation 315

and subsequent generation of the excitation signal 320. The Glottal Pulse 310 comprises an Eigen glottal

pulse that has been identified from the glottal pulse database, the creation of which is described in

further detail in Figure 8 below.

[0026] Figure 4 is a diagram illustrating an embodiment of excitation signal creation for a voiced

segment, indicated generally at 400. It is assumed that a Eigen glottal pulse has been identified from

the glottal pulse database (described in further detail in Figure 7 below). The signal region 405

comprises FO values, which may be predicted by models, from the voiced segment. The lengths of the FO

segments, which may be represented by Nf, are used to determine the length of the excitation signal

using the mathematical equation:

[0027] FO(n) =f * Nf * 5/1000.

[0028] Where represents the sampling frequency of the signal. In a non-limiting example, the value

of 5/1000 represents the interval of 5 ms durations that the FO values are determined for. It should be

noted that any interval of a designated duration of a unit time may be used. Another array, designated

as Fr(n), is obtained by linearly interpolating the FO array.

[0029] From the FO values, glottal boundaries are created, 410, which mark the pitch boundaries of the

excitation signal of the voiced segments in the signal region 405. The pitch period array may be

computed using the following mathematical equation:

[0030] To(n) = Fo'(n)

[0031] Pitch boundaries may then be computed using the determined pitch period array as follows:

[0032] P0 (i) = - To (P°(i -1)

[0033] Where PO (0) =1, i = 1,2,3, ... K, and where P(K+1) just crosses length of the array To(n).

[0034] The glottal pulse 415 is used along with the identified glottal boundaries 410 in the overlap

adding 420 of a glottal pulse beginning at each glottal boundary. The excitation signal 425 is then

created through the process of "stitching", or segment joining, to avoid boundary effects which are

further described in Figures 5 and 6.

[0035] Figure 5 is a diagram illustrating an embodiment of overlap boundaries, indicated generally at

500. The illustration 500 represents a series of glottal pulses 515 and overlapping glottal pulses 520 in

the segment. The vertical axis 505 represents the amplitude of excitation. The horizontal axis 510 may

represent the frame number.

[0036] Figure 6 is a diagram illustrating an embodiment of excitation signal creation for a voiced

segment, indicated generally at 600. "Stitching" may be used to form the final excitation signal of

voiced segments (from Figure 4), which is ideally devoid of boundary effects. In an embodiment, any

number of different excitation signals may have been formed through the overlap add method

illustrated in Figure 4 and in the diagram 500 (Figure 5). The different excitation signals may have a

constantly increasing amount of shifts in glottal boundaries 605 and an equal amount of circular left

shift 630 for the glottal pulse signal. In one embodiment, if the glottal pulse signal 615 is of a length less

than the corresponding pitch period, then the glottal pulse may be zero extended 625 to the length of

the pitch period before circular left shifting 630 is performed. Different arrays of pitch boundaries

(represented as Pm(i), m = 1,2, ... M - 1) are formed with each of the same length as P0 . The arrays

are computed using the following mathematical equation:

[0037] Pm (i) = Po(i) + m * w

[0038] Where w is generally taken as 1msec or, in terms of samples, .For 1000 a sampling frequency of

fs = 16,000, w = 16, for example. The highest pitch period present in the given voice segment is

represented as m * w. Glottal pulses are created and associated with each pitch boundary array Pm

The glottal pulses 620 may be obtained from the glottal pulse signal of some length N by first zero

extending it to the pitch period and then circularly left shifting it by m * w samples.

[0039] For each set of frame boundaries, an excitation signal 635 is formed by initializing the glottal

pulses to zero (0). Overlap add 610 is used to add the glottal pulse 620 to the first N samples of the

excitation, starting from each pitch boundary value of the array Pm(i), i = 1,2,.. K. The formed signal is

as a single stitched excitation, corresponding to the shift, m.

[0040] In an embodiment, the arithmetic mean of all of the single stitched excitation signals is then

computed 640, which represents the final excitation signal for the voiced segment 645.

[0041] Figure 7 is a diagram illustrating an embodiment of glottal pulse identification, indicated

generallyat700. In an embodiment, any two given glottal pulses maybe used to compute the distance

metric/dissimilarity between them. These are taken from the glottal pulse database 840 created in

process 800 (further described in Figure 8 below). The computation may be performed by decomposing

(1) (2) (3) (1) (2) (3 the two given glottal pulses xi,yi into sub-band components x ,x ,x andyi ,yi ,yi . The

given glottal pulse may be transformed into the frequency domain by using a method such as Discrete

Cosine Transform (DCT), for example. The frequency band may be split into a number of bands, which

are demodulated and converted into time domain. In this example, three bands are used for illustrative

purposes.

[0042] The sub-band distance metric is then computed between corresponding sub-band components

of each glottal pulses, denoted as ds(xi(1) , y (1) ). The sub-band metric, which may be represented as

d(f,g), where d, represents the distance between the two sub-band components f and g, may be

computed as described in the following paragraphs.

[0043] The normalized circular cross correlation function between f and g is computed. In one

embodiment, this may be denoted as Rf(n) = f * g, where '*' denotes normalized circular cross

correlation operation between two signals. The period for circular cross correlation is taken to be the highest of lengths of the two signals f and g. The shorter signal is zero extended. The Discrete Hilbert

Transform of normalized circular cross correlation is computed and denoted as Rf(n). Using the

normalized circular cross correlation and the Discrete Hilbert Transform of the normalized circular cross

correlation, the signal may be determined as:

[0044] Hf,g(n)= RF,g(n) 2 + Rq (n)2.

[0045] The cosine of the angle between the two signals f and g may be determined using the

mathematical equation:

[0046] cos(f,g) = maximum value of the signal H,g (n) over all n.

[0047] The sub-band metric, ds(f,g), between the two sub-band components f and gmay be

determined as:

[0048] ds (f, g)= 2(1 - cos 0 (f,g).

[0049] The distance metric between the glottal pulses is finally determined mathematically as:

[0050] d(xi,yi) = d2 (xi ,y) + d (xi ,y) + d (xi ,y 3 )

[0051] The glottal pulse database 840 may be clustered into a number of clusters, for example 256 (or

M), using a modified k-means algorithm 705. Instead of using the Euclidean distance metric, the

distance metric defined above is used. The centroids of a cluster are then updated with that element of

the cluster whose sum of squares of distances from all other elements of that cluster is minimum such

that:

[0052] Dm = lid2 (i,9m) is minimum for m = c, the cluster centroid.

[0053] In an embodiment, the clustering iterations are terminated when there is no shift in any of the

centroids of the k clusters.

[0054] A vector, a set of N real numbers, for example 256, is associated with every glottal pulse 710 in

the glottal pulse database 840 to form a corresponding vector database 715. In one embodiment, the associating is performed for a given glottal pulse xi, a vector

Vi = [w 1 (xi),w2 (xi),w3 (xi), --- (xi), --- 2 s6 (xi)], where T (xi) = d2 (xi, cj) - d2 (XX 0 )_

d 2 (cj,xo) and, x 0 is a fixed glottal pulse picked from the database and d 2 (xi,c) represents the square

of the distance metric defined above between two glottal pulses xi and cj and assuming that

c1, c 2 ,... ci,.. c2 s6 are the centroid glottal pulses determined by clustering.

[0055] Thus, the vector associated with the given glottal pulse xi may be computed with the

mathematical equation:

[0056] Vi = [w 1 (xi),w 2 (xi),w 3 (xi),...j(xi),... 2 s6 (xi)

[0057] In step 720, Principal Component Analysis (PCA) is performed to compute Eigenvectors of the

vector database 715. In one embodiment, any one Eigenvector may be chosen 725. The closest

matching vector 730 to the chosen Eigenvector from the vector database 715 is then determined in the

sense of Euclidean distance. The glottal pulse from the pulse database 840 which corresponds to the

closest matching vector 730 is regarded as the resulting Eigen glottal pulse 735 associated with an

Eigenvector.

[0058] Figure 8 is a diagram illustrating an embodiment of glottal pulse database creation indicated

generally at 800. A speech signal, 805, undergoes pre-filtering, such as pre-emphasis 810. Linear

Prediction (LP) Analysis, 815, is performed using the pre-filtered signal to obtain the LP coefficients.

Thus, low frequency information of the excitation may be captured. Once the coefficients are

determined, they are used to inverse filter, 820, the original speech signal, 805, which is not pre-filtered,

to compute the Integrated Linear Prediction Residual (ILPR) signal 825. The ILPR signal 825 may be used

as an approximation to the excitation signal, or voice source signal. The ILPR signal 825 is segmented

835 into glottal pulses using the glottal segment/cycle boundaries that have been determined from the

speech signal 805. The segmentation 835 may be performed using the Zero Frequency Filtering

Technique (ZFF) technique. The resulting glottal pulses may then be energy normalized. All of the glottal pulses for the entire speech training data are combined in order to form the glottal pulse database 840.

[0059] While the invention has been illustrated and described in detail in the drawings and foregoing

description, the same is to be considered as illustrative and not restrictive in character, it being

understood that only the preferred embodiment has been shown and described and that all equivalents,

changes, and modifications that come within the spirit of the invention as described herein and/or by

the following claims are desired to be protected.

[0060] Hence, the proper scope of the present invention should be determined only by the broadest

interpretation of the appended claims so as to encompass all such modifications as well as all

relationships equivalent to those illustrated in the drawings and described in the specification.

[0061] In the specification and the claims the term "comprising" shall be understood to have a broad

meaning similar to the term "including" and will be understood to imply the inclusion of a stated integer

or step or group of integers or steps but not the exclusion of any other integer or step or group of

integers or steps. This definition also applies to variations on the term "comprising" such as "comprise"

and "comprises".

[0062] The reference to any prior art in this specification is not, and should not be taken as an

acknowledgement or any form of suggestion that the referenced prior art forms part of the common

general knowledge in Australia.

Claims (3)

1. A method to create a glottal pulse database from a speech signal, in a speech synthesis system,

wherein the system comprises at least a glottal pulse database, comprising the steps of:

a. pre-emphasizing the speech signal to obtain a pre-filtered signal;

b. analyzing the pre-filtered signal, using linear prediction, to obtain inverse filtering

parameters;

c. performing inverse filtering of the speech signal using the inverse filtering parameters;

d. determining an integrated linear prediction residual signal using the inversely filtered

speech signal;

e. identifying glottal segment boundaries in the speech signal;

f. segmenting the integrated linear prediction residual signal into glottal pulses using the

identified glottal segment boundaries from the speech signal;

g. normalizing the glottal pulses;

h. forming the glottal pulse database by collecting all normalized glottal pulses obtained

for the speech signal; and

i. applying the formed glottal pulse database to form an excitation signal, wherein the

excitation signal is applied in the speech synthesis system to synthesize speech.

2. The method of claim 1, wherein the inverse filtering parameters in step (b) comprise linear

prediction coefficients.

3. The method of claim 1, wherein the identifying of step (e) is performed using Zero Frequency

Filtering technique.