System and method for selecting training text

• US 6,038,533 A
• Filed: 07/07/1995
• Issued: 03/14/2000
• Est. Priority Date: 07/07/1995
• Status: Expired due to Term

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Buchsbaum, Adam Louis, VanSanten, Jan Pieter
• Primary Examiner(s): Isen, Forester W.
• Assistant Examiner(s): EDOUARD, PATRICK NESTOR
• Application Number:
  US08/499,159
• Time in Patent Office: 1,712 Days
• Field of Search: 395/2.67, 395/2.69, 395/2.58, 395/2.63, 395/2.64-2.66
• US Class Current: 704/260
• CPC Class Codes: G10L 13/027 Concept to speech synthesisers; Generation of natural phrases from machine-based concepts generation of parameters for speech synthesis out of text G10L13/08
• CPC Class Codes: G10L 13/04 Details of speech synthesis systems, e.g. synthesiser structure or memory management

Abstract

A system and method are described for determining a near-optimum subset of data, based on a selected model, from a large corpus of data. Sets of feature vectors corresponding to natural or other preselected divisions of the data corpus are mapped into matrices representative of such divisions. The invention operates to find a submatrix of full rank formed as a union of one or more of those division-based matrices. A greedy algorithm utilizing Gram-Schmidt orthonormalization operates on the division matrices to find a near optimum submatrix and in a time bound representing a substantial improvement over prior-art methods. An important application of the invention is the selection of a small number of sentences from a corpus of a very large number of such sentences from which the parameters of a duration model for speech synthesis can be estimated.

Claims

1. A method for identifying a subset of a corpus of speech data usable for estimating speech parameters in a speech processing application, said corpus being arranged as a plurality of sentences, comprising the steps of:

constructing feature vectors corresponding to all phonetic segments appearing in said corpus;

mapping said feature vectors into a plurality of matrices based on a model chosen to fit said corpus, said matrices being arranged to include sets of said feature vectors corresponding to sentences in said corpus; and

operating on said parameter space matrices with a greedy algorithm to find a submatrix of full rank, said full-rank submatrix being formed by the union of one or more of said model-based matrices and whereby sentences corresponding to said one or more of said model-based matrices included in said full-rank submatrix comprise said subset of said corpus of speech data;

wherein an articulation of one or more of said corresponding sentences provides an input to said speech processing application for estimation of said speech parameters.

2. The speech parameter estimation method of claim 1 wherein duration parameters for a plurality of phonetic segments are estimated.

3. The speech parameter estimation method of claim 1 wherein said model chosen to fit said corpus is a linear model.

4. The speech parameter estimation method of claim 1 wherein said greedy algorithm includes orthonormalization of said speech feature vectors.

5. The speech parameter estimation method of claim 4 wherein said greedy algorithm is of the form ##EQU23##

6. A system for identifying a subset of a corpus of speech data usable for estimating speech parameters in a speech processing application, said corpus being arranged as a plurality of sentences, comprising:

means for constructing feature vectors corresponding to all phonetic segments appearing in said corpus;

means for mapping said feature vectors into a plurality of matrices based on a model selected to fit said corpus, said matrices being arranged to include sets of said feature vectors corresponding to sentences in said corpus; and

means for applying a greedy algorithm to said model-based matrices for finding a submatrix of full rank, said full-rank submatrix being formed by the union of one or more of said model-based matrices and whereby sentences corresponding to said one or more of said model-based matrices included in said full-rank submatrix comprise said subset of said corpus of speech data;

wherein an articulation of one or more of said corresponding sentences provides an input to said speech processing application for estimation of said speech parameters.

7. The speech parameter estimation system of claim 6 wherein said greedy algorithm includes orthonormalization of said feature vectors.

8. The speech parameter estimation system of claim 7 wherein said greedy algorithm is of the form ##EQU24##

9. In a method for synthesizing speech from text comprising the steps of:

analyzing input text to determine phonetic segments for said input text;

estimating acoustic parameters associated with each said phonetic segment; and

generating a speech waveform based on said estimated acoustic parameters to synthesize said input text into speech;

wherein said acoustic parameters determined in said estimating step are derived from a set of training data, and said training data are manifested as a set of sentences selected from a corpus of speech data arranged as a plurality of sentences;

a method for selecting said selected sentences comprising the steps of;

constructing feature vectors corresponding to all phonetic segments appearing in said corpus;

mapping said feature vectors into a plurality of matrices based on a model chosen to fit said corpus, said matrices arranged to include sets of said feature vectors corresponding to sentences in said corpus; and

operating on said model-based matrices with a greedy algorithm to find a submatrix of full rank, said full-rank submatrix being formed as the union of one or more of said model-based matrices, whereby sentences corresponding to said one or more of said model-based matrices included in said full-rank submatrix comprise said selected sentences.

10. The text-to-speech synthesis method of claim 9 wherein said estimated acoustic parameters include duration parameters for a plurality of phonetic segments.

11. The text-to-speech synthesis method of claim 9 wherein said chosen model is a linear model.

12. The text-to-speech synthesis method of claim 9 wherein said greedy algorithm includes orthonormalization of said feature vectors.

13. The text-to-speech synthesis method of claim 12 wherein said greedy algorithm is of the form ##EQU25##

14. In a system for synthesizing speech from text comprising:

a text analysis means for analyzing input text to determine phonetic segments for said input text;

parameter estimation means for estimating acoustic parameters associated with each said phonetic segment; and

speech generation means for generating a speech waveform based on said estimated speech parameters to thereby synthesize said input text into speech;

wherein said parameter estimation means further includes means for deriving a set of training data, said training data being manifested as a set of sentences selected from a corpus of speech data arranged as a plurality of sentences, and said means for deriving a set of training data further comprises;

means for constructing feature vectors corresponding to all phonetic segments appearing in a plurality of sentences;

means for mapping said feature vectors into a plurality of matrices based on a model chosen to fit said plurality of sentences, said matrices being arranged to include sets of said feature vectors corresponding to sentences in said plurality of sentences;

means for applying a greedy algorithm to said model-based matrices for finding a submatrix of full rank, said full-rank submatrix being formed as the union of one or more of said model-based matrices.

15. The text-to-speech synthesis system of claim 14 wherein said greedy algorithm includes orthonormalization of said feature vectors.

16. The text-to-speech synthesis system of claim 14 wherein said greedy algorithm is of the form ##EQU26##

17. A method for selecting speech parameter estimation sentences to be applied in a speech processing application by analyzing each of a plurality of sentences, said plurality of sentences including said selected speech parameter estimation sentences, according to the following steps:

constructing feature vectors corresponding to all phonetic segments appearing in said plurality of sentences;

mapping said feature vectors into a plurality of matrices based on a model chosen to fit said plurality of sentences, said matrices being arranged to include sets of said feature vectors corresponding to sentences in said plurality of sentences; and

operating on said model-based matrices with a greedy alogorithm to find a submatrix of full rank, said full-rank submatrix being formed by the union of one or more of said model-based matrices, the sentences corresponding to said one or more of said model-based matrices comprising said full-rank submatrix being selected as said speech parameter estimation sentences;

wherein an articulation of one or more of said speech parameter estimation sentences provides an input to said speech processing application for estimation of said speech parameters.

18. The speech parameter estimation sentence selection method of claim 17 wherein said estimation sentences enable the prediction of duration parameters for a plurality of phonetic segments.

19. The speech parameter estimation sentence selection method of claim 17 wherein said model chosen to fit said plurality of sentences is a linear model.

20. The speech parameter estimation sentence selection method of claim 17 wherein said greedy algorithm includes orthonormalization of said feature vectors.

21. The speech parameter estimation sentence selection method of claim 20 wherein said greedy algorithm is of the form ##EQU27##

22. A set of test sentences for estimation of speech parameters selected according to the method of claim 17.

23. A model for estimation of speech parameters characterized as being populated in accordance with data derived from speech parameter estimation sentences selected according to the method of claim 17.

24. A storage means fabricated to contain a set of speech parameter estimation sentences selected in accordance with the method of claim 17.

25. A storage means fabricated to contain a model for estimation of speech parameters, said model characterized as being populated in accordance with data derived from speech parameter estimation sentences selected according to the method of claim 17.

26. A method for estimating speech parameters in a speech processing application by use of a model populated from data derived from a selected set of speech parameter estimation sentences, said speech parameter estimation sentences having been selected according to the following steps:

constructing feature vectors corresponding to all phonetic segments appearing in a plurality of sentences, said plurality of sentences including said selected speech parameter estimation sentences;

mapping said feature vectors into a plurality of matrices based on said model, said matrices being arranged to include sets of said feature vectors corresponding to sentences in said plurality of sentences; and

operating on said model-based matrices with a greedy algorithm to find a submatrix of full rank, said full-rank submatrix being formed by the union of one or more of said model-based matrices, the sentences corresponding to said one or more of said model-based matrices comprising said full-rank submatrix being selected as said speech parameter estimation sentences;

wherein an articulation of one or more of said speech parameter estimation sentences provides an input to said speech-parameter-estimation model.

27. The method for estimating speech parameters of claim 26 wherein said selection of said speech parameter estimation sentences estimation sentences is further characterized by said model being a linear model.

28. The method for estimating speech parameters of claim 26 wherein said selection of said speech parameter estimation sentences estimation sentences is further characterized by said greedy algorithm including orthonormalization of said feature vectors.

29. The method for estimating speech parameters of claim 28 wherein said selection of said speech parameter estimation sentences estimation sentences is further characterized by said greedy algorithm being of the form ##EQU28##30.

30. A storage means fabricated to contain a set of instructions corresponding to the method of claim 26.

31. A method for identifying a subset of a corpus of speech data usable for estimating speech parameters in a speech processing application, said corpus being arranged as a plurality of ordered word sets, said word ordering being in accordance with a known ordering methodology, said method comprising the steps of:

constructing feature vectors corresponding to all phonetic segments appearing in said corpus;

mapping said feature vectors into a plurality of matrices based on a model chosen to fit said corpus, said matrices being arranged to include sets of said feature vectors corresponding to word sets in said corpus; and

operating on said parameter space matrices with a greedy algorithm to find a submatrix of full rank, said full-rank submatrix being formed by the union of one or more of said model-based matrices and whereby word sets corresponding to said one or more of said model-based matrices included in said full-rank submatrix comprise said subset of said corpus of speech data;

wherein an articulation of one or more of said corresponding word sets provides an input to said speech processing application for estimation of said speech parameters.