Method and apparatus for predicting word prominence in speech synthesis

• US 7,778,819 B2
• Filed: 12/04/2007
• Issued: 08/17/2010
• Est. Priority Date: 05/14/2003
• Status: Expired due to Fees

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Bellegarda, Jerome R., Silverman, Kim E. A.
• Primary Examiner(s): Chawan; Vijay B
• Application Number:
  US11/999,323
• Publication Number:
  US 20080091430A1
• Time in Patent Office: 987 Days
• Field of Search: 704/9, 704/260, 704/257, 704/277, 704/258, 704/266, 704/267, 704/3, 704/2, 704/10, 704/255, 704/231, 704/251, 704/268, 704/269, 704/261
• US Class Current: 704/9
• 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. Determining whether information in a current sentence is new or previously given is performed based on a semantic relationship between the current sentence and a number of preceding sentences. A word prominence for the synthetic speech to a word in the current sentence is assigned in accordance with the information determination. A speech representative of the current sentence can be generated. 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:

determining, through a processor, whether an information in a current sentence is new or previously given based on a semantic relationship in a vector space between the current sentence and a number of preceding sentences;

assigning, through the processor, a word prominence for the synthetic speech to a word in the current sentence in accordance with the information determination; and

generating, through an output device, a speech representative of the current sentence.

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 sentence 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 to determine closeness measures; 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 a first closeness measure of the closeness measures semantic anchor associated with the number of preceding sentences and a second closeness measure of 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 storing executable instructions that, when accessed by a machine, cause the machine to

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

assign, through the processor, a word prominence, for synthetic speech, to a word in the current sentence in accordance with the information determination; and

generate, through an output device, a speech representative of the current sentence.

12. The article of manufacture of claim 11, wherein the executable instructions, when accessed, further cause 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 executable instructions, when accessed, further cause the machine, when determining the semantic relationship using LSA, to:

generate a word prominence assignment model comprising semantic anchors associated with the current sentence 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 executable instructions, when accessed, further cause 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 to determine closeness measures; 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 executable instructions, when accessed, further cause the machine, when determining the novelty score from the closeness measures, to:

compute a content prediction index from a first closeness measure of the closeness measures of the semantic anchor associated with the number of preceding sentences and a second closeness measure of 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 executable instructions, when accessed, further cause 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 executable instructions, when accessed, further cause 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.