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

• US 20150348535A1
• Filed: 05/28/2014
• Published: 12/03/2015
• Est. Priority Date: 05/28/2014
• Status: Active Grant

• Current Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Original Assignee: Interactive Intelligence Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Ganapathiraju, Aravind, Dachiraju, Rajesh
• Granted Patent: US 10,014,007 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G10L 13/02 Methods for producing synthetic speech; Speech synthesisers
• CPC Class Codes: G10L 25/90 Pitch determination of speech signals

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.

Claims

1. A method to create a glottal pulse database from a speech signal, comprising the steps of:

a. performing pre-filtering on the speech signal to obtain a pre-filtered signal;

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

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

d. computing 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. performing normalization of the glottal pulses; and

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

2. The method of claim 1, wherein the analysis of step (b) is performed using linear prediction.

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

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

5. The method of claim 1, wherein the pre-filtering of step (a) comprises pre-emphasis.

6. A method to form parametric models, comprising the steps of:

a. computing a glottal pulse distance metric between a number of glottal pulses;

b. clustering the glottal pulse database into a number of clusters to determine centroid glottal pulses;

c. 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;

d. determining Eigenvectors of the vector database; and

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

7. The method of claim 6, wherein the number of glottal pulses is two.

8. The method of claim 6, wherein step (a) further comprises the steps of:

a. de-composing the number of glottal pulses into corresponding sub-band components;

b. computing a sub-band distance metric between the corresponding sub-band components of each glottal pulse; and

c. computing the glottal pulse distance metric mathematically using the sub-band distance metrics.

9. The method of claim 8, wherein the computing of step (c) is performed using the mathematical equation:

10. The method of claim 6, wherein the number of clusters is 256.

11. The method of claim 6, wherein the clustering of step (b) is performed using a modified k-means calculation that utilizes the glottal pulse distance metric.

12. The method of claim 11, wherein the modified k-means calculation further comprises updating a centroid of a cluster with an element of the cluster whose sum of squares of distances from all other elements of that cluster is minimum.

13. The method of claim 12, further comprising terminating the clustering iterations when there is no shift in any of the centroids from the clusters.

14. The method of claim 6, wherein the determining of Eigenvectors of step (d) is performed using Principal Component Analysis.

15. The method of claim 6, wherein step (e) further comprises the steps of:

a. determining the Eigenvector;

b. determining the closest matching vector from the vector database to the Eigenvector

c. determining the closest matching glottal pulse from the glottal pulse database; and

d. naming the glottal pulse from the pulse database that is the closest match to the Eigenvector as the Eigen glottal pulse associated with the Eigenvector.

16. The method of claim 6, further comprising the step of training the formed parametric models for use in speech synthesis.

17. The method of claim 16, wherein the training further comprises the steps of:

a. defining a training text corpus;

b. obtaining speech data by recording a voice talent speaking the training text;

c. converting the training text into context dependent phone labels;

d. determining the spectral features of the speech data using the phone labels;

e. estimating the fundamental frequency of the speech data; and

f. performing parameter estimation on an audio stream using the spectral features, the fundamental frequency, and the duration of the audio stream.

18. A method 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.

19. The method of claim 18, wherein the step of creating an excitation signal further comprises the steps of:

a. dividing signal regions of excitation into categories of segments; and

b. creating an excitation signal for each category;

20. The method of claim 19, wherein the categories of segments comprise one or more of:

voiced, unvoiced, and pause.

21. The method of claim 19, wherein the dividing is performed based on the fundamental frequency value.

22. The method of claim 18, wherein the filter of step (d) comprises a Mel Log Spectrum Approximation filter.

23. The method of claim 20, wherein the step of creating an excitation signal comprises placing white noise in the unvoiced segments.

24. The method of claim 20, wherein the step of creating an excitation signal for pause segments comprises placing a zero in the segment.

25. The method of claim 20, wherein the excitation signal is created for voiced segments comprising the steps of:

a. creating glottal boundaries, using the predicted fundamental frequency value from a model, wherein the glottal boundaries mark pitch boundaries of the excitation signal;

b. adding a glottal pulse beginning at each glottal boundary using an overlap add method;

c. avoiding boundary effects in the excitation signal wherein the avoiding further comprises the steps of;

i. creating a number of different excitations formed through the overlap add method with a constantly increasing amount of shifts in the glottal boundaries and an equal amount of circular left shift for the glottal pulse, wherein if the glottal pulse is of a length less than the corresponding pitch period, then the glottal pulse is zero extended to the length of pitch period prior to the left shift,

ii. determining the arithmetic mean of the number of different excitation signals, and

iii. declaring the arithmetic mean the final excitation signal for the voiced segment.

26. The method of claim 18, wherein the Eigen glottal pulse is identified from a glottal pulse database, the identification comprising the steps of:

a. computing a glottal pulse distance metric between a number of glottal pulses;

b. clustering the glottal pulse database into a number of clusters to determine centroid glottal pulses;

c. 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;

d. determining Eigenvectors of the vector database; and

e. forming parametric models by associating a glottal pulse from the glottal pulse database to each determined Eigenvector to form parametric models.

27. The method of claim 26, wherein the number of glottal pulses is two.

28. The method of claim 26, wherein step (a) further comprises the steps of:

a. de-composing the number of glottal pulses into corresponding sub-band components;

b. computing a sub-band distance metric between the corresponding sub-band components of each glottal pulse; and

c. computing the distance metric mathematically using the sub-band distance metrics.

29. The method of claim 28, wherein the computing of step (c) is performed using the mathematical equation:

30. The method of claim 26, wherein the number of clusters is 256.

31. The method of claim 26, wherein the clustering of step (b) is performed using a modified k-means calculation that utilizes the glottal pulse distance metric.

32. The method of claim 31, wherein the modified k-means calculation further comprises updating a centroid of a cluster with an element of the cluster whose sum of squares of distances from all other elements of that cluster is minimum.

33. The method of claim 32, further comprising terminating the clustering iterations when there is no shift in any of the centroids from the clusters.

34. The method of claim 26, wherein the determining of Eigenvectors of step (d) is performed using Principal Component Analysis.

35. The method of claim 26, wherein step (e) further comprises the steps of:

a. determining the Eigenvector;

b. determining the closest matching vector from the vector database to the Eigenvector;

c. determining the closest matching glottal pulse from the glottal pulse database; and

d. naming the glottal pulse from the pulse database that is the closest match to the Eigenvector as the Eigen glottal pulse associated with the Eigenvector.

36. The method of claim 26, further comprising building the glottal pulse database from a speech signal, the building comprising the steps of:

a. performing pre-filtering of the speech signal to obtain a pre-filtered signal;

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

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

d. computing 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. performing normalization of the glottal pulses; and

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

37. The method of claim 36, wherein the analysis of step (b) is performed using linear prediction.

38. The method of claim 36, wherein the inverse filtering parameters in step (b) comprise linear prediction coefficients.

39. The method of claim 36, wherein the identifying of step (e) is performing using Zero Frequency Filtering technique.

40. The method of claim 36, wherein the pre-filtering of step (a) comprises pre-emphasis.