Method and apparatus for predicting word prominence in speech synthesis

• US 20080091430A1
• Filed: 12/04/2007
• Published: 04/17/2008
• Est. Priority Date: 05/14/2003
• Status: Active Grant

• Current Assignee: Jerome Bellegarda, Kim Silverman
• Original Assignee: Jerome Bellegarda, Kim Silverman
• Inventors: Bellegarda, Jerome, Silverman, Kim
• Granted Patent: US 7,778,819 B2
• Time in Patent Office: Days
• US Class Current: 704/258
• CPC Class Codes: G10L 13/033 Voice editing, e.g. manipulating the voice of the synthesiser
• CPC Class Codes: G10L 13/04 Details of speech synthesis systems, e.g. synthesiser structure or memory management

Abstract

A method and apparatus is provided for generating speech that sounds more natural. In one embodiment, word prominence and latent semantic analysis are used to generate more natural sounding speech. A method for generating speech that sounds more natural may comprise generating synthesized speech having certain word prominence characteristics and applying a semantically-driven word prominence assignment model to specify word prominence consistent with the way humans assign word prominence.

Claims

1. A method for assigning word prominence in synthetic speech comprising:

generating a speech representative of a current sentence;

determining whether an information in the current sentence is new or previously given in accordance with a semantic relationship between the current sentence and a number of preceding sentences; and

assigning a word prominence to a word in the current sentence in accordance with the information determination.

2. The method of claim 1, further comprising:

determining the semantic relationship between the current sentence and the number of preceding sentences using latent semantic analysis (LSA).

3. The method of claim 2, wherein determining the semantic relationship using LSA includes:

generating a word prominence assignment model comprising semantic anchors associated with the current and the number of preceding sentences; and

classifying each word in the current sentence against the semantic anchors to determine whether the word represents the new or previously given information.

4. The method of claim 3, wherein classifying each word in the current sentence against the semantic anchors includes:

measuring a closeness between a vector representing the word and the semantic anchors; and

determining a novelty score from the closeness measures, wherein the novelty score has a first value when the information is new and a second value when the information is previously given.

5. The method of claim 4, wherein the first value is a positive value and the second value is a negative value.

6. The method of claim 4, wherein the first value is a negative value and the second value is a positive value.

7. The method of claim 4, wherein determining the novelty score from the closeness measures includes:

computing a content prediction index from the closeness measure of the semantic anchor associated with the number of preceding sentences and the closeness measures of the semantic anchors associated with the current sentence; and

inverting the content prediction index.

8. The method of claim 1, wherein assigning a word prominence to a word in the current sentence includes:

emphasizing the word in the current sentence when the word represents the new information; and

de-emphasizing the word in the current sentence when the word represents the previously given information.

9. The method of claim 8, wherein emphasizing and de-emphasizing is achieved through altering a prosodic feature of the word.

10. The method of claim 9, wherein altering the prosodic feature includes altering at least one of volume, pitch, and phoneme duration.

11. An article of manufacture comprising:

a machine accessible medium providing content that, when accessed by a machine, causes the machine to

generate a speech representative of a current sentence;

determine whether an information in the current sentence is new or previously given in accordance with a semantic relationship between the current sentence and a number of preceding sentences; and

assign a word prominence to a word in the current sentence in accordance with the information determination.

12. The article of manufacture of claim 11, wherein the content, when accessed, further causes the machine to determine the semantic relationship between the current sentence and the number of preceding sentences using latent semantic analysis (LSA).

13. The article of manufacture of claim 12, wherein the content, when accessed, further causes the machine, when determining the semantic relationship using LSA, to:

generate a word prominence assignment model comprising semantic anchors associated with the current and the number of preceding sentences; and

classify each word in the current sentence against the semantic anchors to determine whether the word represents the new or previously given information.

14. The article of manufacture of claim 13, wherein the content, when accessed, further causes the machine, when classifying each word in the current sentence against the semantic anchors, to:

measure a closeness between a vector representing the word and the semantic anchors; and

determine a novelty score from the closeness measures, wherein the novelty score has a first value when the information is new and a second value when the information is previously given.

15. The article of manufacture of claim 14, wherein the first value is a positive value and the second value is a negative value.

16. The article of manufacture of claim 14, wherein the first value is a negative value and the second value is a positive value.

17. The article of manufacture of claim 14, wherein the content, when accessed, further causes the machine, when determining the novelty score from the closeness measures, to:

compute a content prediction index from the closeness measure of the semantic anchor associated with the number of preceding sentences and the closeness measures of the semantic anchors associated with the current sentence; and

invert the content prediction index.

18. The article of manufacture of claim 11, wherein the content, when accessed, further causes the machine, when assigning a word prominence to a word in the current sentence, to:

emphasize the word in the current sentence when the word represents the new information; and

de-emphasize the word in the current sentence when the word represents the previously given information.

19. The article of manufacture of claim 18, wherein the content, when accessed, further causes the machine, when emphasizing and de-emphasizing, to alter a prosodic feature of the word.

20. The article of manufacture of claim 19, wherein altering the prosodic feature includes altering at least one of volume, pitch, and phoneme duration.