IP Library Granted Patent US 9,484,045
Granted Patent B2
US 9,484,045 · App. 13/606,618 · Granted Nov 1, 2016

System and method for automatic prediction of speech suitability for statistical modeling

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Quick Facts
Patent No.
US 9,484,045
App. No.
13/606,618
Granted
Nov 1, 2016
Kind
B2
Abstract

An embodiment according to the invention provides a capability of automatically predicting how favorable a given speech signal is for statistical modeling, which is advantageous in a variety of different contexts. In Multi-Form Segment (MFS) synthesis, for example, an embodiment according to the invention uses prediction capability to provide an automatic acoustic driven template versus model decision maker with an output quality that is high, stable and depends gradually on the system footprint. In speaker selection for a statistical Text-to-Speech synthesis (TTS) system build, as another example context, an embodiment according to the invention enables a fast selection of the most appropriate speaker among several available ones for the full voice dataset recording and preparation, based on a small amount of recorded speech material.

Claims (35)

1. A computer server system for automatically determining suitability of at least a portion of a speech signal, comprising voice data, for statistical modeling, the system comprising:

a memory storing computer code instructions thereon; and

a processor,

the memory, with the computer code instructions, and the processor being configured to cause the computer server system to implement:

a modelability estimator configured to:

determine a statistical modelability score of the at least a portion of the speech signal comprising voice data, the statistical modelability score indicating favorability of the at least a portion of the speech signal for statistical modeling in terms of human perception and based at least in part on determining a temporal stationarity of the at least a portion of the speech signal comprising voice data; and

forward the statistical modelability score to a speech synthesis system executed by the processor, wherein the speech synthesis system is configured to utilize the modelability score in converting text to speech; and

a decision maker configured to determine a preferred speaker selection for use by the speech synthesis system in building a statistical text-to-speech system based on the statistical modelability score determined for speech provided by each of a plurality of speakers.

2. The computer server system according to claim 1 , wherein the modelability estimator is further configured to determine the temporal stationarity based on variability of an instantaneous spectrum of the at least a portion of the speech signal.

3. The computer server system according to claim 2 , wherein the modelability estimator is still further configured to determine the variability of the instantaneous spectrum based on (i) a first moment of an instantaneous spectrum component distribution and (ii) a second moment of the instantaneous spectrum component distribution.

4. The computer server system according to claim 1 , wherein the decision maker is further configured to:

determine a segment representation type to be used by the speech synthesis system in a multi-form segment speech synthesis based on the statistical modelability score.

5. The computer server system according to claim 4 , wherein the modelability estimator is further configured to determine the statistical modelability score for at least one segment comprising at least a portion of an output speech signal being synthesized, and wherein the decision maker is further configured to determine the segment representation type, for the at least one segment, based on at least the statistical modelability score for the at least one segment.

6. The computer server system according to claim 4 , wherein the modelability estimator is further configured to determine for at least one segment comprising at least a portion of an output speech signal being synthesized, the statistical modelability score for a segment cluster that includes the at least one segment, and wherein the decision maker is further configured to determine the segment representation type, for the at least one segment, based on at least the statistical modelability score of the segment cluster that includes the at least one segment.

7. The computer server system according to claim 4 , further comprising a templates pruner configured to remove from a voice dataset at least one segment relative to its statistical modelability score.

8. The computer server system according to claim 4 , wherein the statistical modelability score is further based at least in part on a loudness score.

9. A computerized method of automatically determining, by a server, suitability of at least a portion of a speech signal, comprising voice data, for statistical modeling, the computerized method comprising:

determining a statistical modelability score of the at least a portion of the speech signal comprising voice data, the statistical modelability score indicating favorability of the at least a portion of the speech signal for statistical modeling in terms of human perception and based at least in part on a temporal stationarity of the at least a portion of the speech signal comprising voice data;

forwarding the statistical modelability score to a speech synthesis system implemented by the server, wherein the speech synthesis system is configured to utilize the modelability score in converting text to speech; and

determining a preferred speaker selection for use by the speech synthesis system in building a statistical text-to-speech system based on the statistical modelability score determined for speech provided by each of a plurality of speakers.

10. The computerized method according to claim 9 , wherein the temporal stationarity is determined based on variability of an instantaneous spectrum of the at least a portion of the speech signal.

11. The computerized method according to claim 10 , wherein the variability of the instantaneous spectrum is determined based on (i) a first moment of an instantaneous spectrum component distribution and (ii) a second moment of the instantaneous spectrum component distribution.

12. The computerized method according to claim 9 , wherein the method comprises determining a segment representation type to be used by the speech synthesis system in a multi-form segment speech synthesis system based on the statistical modelability score.

13. The computerized method according to claim 12 , further comprising:

determining the statistical modelability score for at least one segment comprising at least a portion of an output speech signal being synthesized; and

determining the segment representation type, for the at least one segment, based on at least the statistical modelability score for the at least one segment.

14. The computerized method according to claim 12 , further comprising:

determining, for at least one segment comprising at least a portion of an output speech signal being synthesized, the statistical modelability score for a segment cluster that includes the at least one segment; and

determining the segment representation type, for the at least one segment based on at least the statistical modelability score of the segment cluster that includes the at least one segment.

15. The computerized method according to claim 14 , further comprising removing from a voice dataset at least one segment relative to its statistical modelability score.

16. The computerized method according to claim 12 , further comprising determining the statistical modelability score based at least in part on a loudness score.

17. A non-transitory computer-readable storage medium having computer-readable code stored thereon, which, when executed by a computer processor, causes the computer processor to automatically determine suitability of at least a portion of a speech signal, comprising voice data, for statistical modeling, by causing the processor to:

determine a statistical modelability score of the at least a portion of the speech signal comprising voice data, the statistical modelability score indicating favorability of the at least a portion of the speech signal for statistical modeling in terms of human perception and the statistical modelability score being based at least in part on a temporal stationarity of the at least a portion of the speech signal comprising voice data;

forward the statistical modelability score to a speech synthesis system executed by the processor, wherein the speech synthesis system is configured to utilize the modelability score in converting text to speech; and

determine a preferred speaker selection for use by the speech synthesis system in building a statistical text-to-speech system based on the statistical modelability score determined for speech provided by each of a plurality of speakers.

Assignments (8)
RELEASE (REEL 052935 / FRAME 0584) Recorded Jan 2, 2025
From: WELLS FARGO BANK, NATIONAL ASSOCIATION
To: CERENCE OPERATING COMPANY
Reel/Frame 069797/0818 →
CORRECTIVE ASSIGNMENT TO CORRECT THE REPLACE THE CONVEYANCE DOCUMENT WITH THE NEW ASSIGNMENT PREVIOUSLY RECORDED AT REEL: 050836 FRAME: 0191. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Apr 19, 2022
From: NUANCE COMMUNICATIONS, INC.
To: CERENCE OPERATING COMPANY
Reel/Frame 059804/0186 →
SECURITY AGREEMENT Recorded Jun 15, 2020
From: CERENCE OPERATING COMPANY
To: WELLS FARGO BANK, N.A.
Reel/Frame 052935/0584 →
RELEASE OF SECURITY INTEREST Recorded Jun 12, 2020
From: BARCLAYS BANK PLC
To: CERENCE OPERATING COMPANY
Reel/Frame 052927/0335 →
SECURITY AGREEMENT Recorded Nov 7, 2019
From: CERENCE OPERATING COMPANY
To: BARCLAYS BANK PLC
Reel/Frame 050953/0133 →
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE NAME PREVIOUSLY RECORDED AT REEL: 050836 FRAME: 0191. ASSIGNOR(S) HEREBY CONFIRMS THE INTELLECTUAL PROPERTY AGREEMENT. Recorded Oct 29, 2019
From: NUANCE COMMUNICATIONS, INC.
To: CERENCE OPERATING COMPANY
Reel/Frame 050871/0001 →
INTELLECTUAL PROPERTY AGREEMENT Recorded Oct 23, 2019
From: NUANCE COMMUNICATIONS, INC.
To: CERENCE INC.
Reel/Frame 050836/0191 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 10, 2012
From: SORIN, ALEXANDER; SHECHTMAN, SLAVA; POLLET, VINCENT
To: NUANCE COMMUNICATIONS, INC.
Reel/Frame 028926/0372 →