METHOD AND APPARATUS FOR PREPARING A DOCUMENT TO BE READ BY TEXT-TO-SPEECH READER

• US 20090099846A1
• Filed: 12/19/2008
• Published: 04/16/2009
• Est. Priority Date: 06/28/2002
• Status: Active Grant

• Current Assignee: Cerence Operating Company (Cerence Inc.)
• Original Assignee: International Business Machines Corporation
• Inventors: Pickering, John B.
• Granted Patent: US 7,953,601 B2
• Time in Patent Office: Days
• US Class Current: 704/260
• CPC Class Codes: G10L 13/08 Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination

Abstract

There is disclosed a method and system for preparing a document to be read by a text-to-speech reader. The method can include identifying two or more voice types available to the text-to-speech reader, identifying the text elements within the document, grouping related text elements together, and classifying the text elements according to voice types available to the text-to-speech reader. The method of grouping the related text elements together can include syntactic and intelligent clustering. The classification of text elements can include performing latent semantic analysis on the text elements and characteristics of the available voice types.

Claims

1. A system for automatically marking a document to be read by a text-to-speech reader with voice type identifiers, said system comprising:

means for identifying two or more voice types available to the text-to-speech reader, each voice type having a corresponding voice type identifier;

means for identifying text elements within the document, wherein the means for identifying text elements is configured to mark gross structural subdivisions of text with a first set of sequenced tags, mark individual paragraphs of the text with a second set of sequenced tags, and mark text elements with a third set of sequenced tags to generate a hierarchical tree identifying the text elements;

means for grouping similar text elements together, wherein said means groups similar text elements by generating one or more clusters according to each identifiable topic of the document and by syntactically parsing the document and subsequently performing text mining to determine which text elements in the document are similar, wherein similarity is based upon lexical affinities among the text elements;

means for classifying the grouped text elements according to voice types available to the text-to-speech reader; and

means for marking the classified grouped text elements within the document with corresponding voice type identifiers.

2. The system as claimed in claim 1, wherein the means for identifying text elements comprise means for breaking down the document into elements and means for separating out the text elements.

3. The system as claimed in claim 1, wherein the means for grouping similar text elements together comprise means for parsing for structural features of the text elements.

4. The system as claimed in claim 3, wherein the structural features of the text elements include at least one of the position of the text element in the document, the syntax of the text element, and text features within the text element.

5. The system as claimed in claim 3, wherein the means for grouping similar text elements further comprise means for parsing for thematic features of the text elements.

6. The system as claimed in claim 1, wherein the means for classifying the text elements according to the available voice types comprise means for finding the best match between the grouped text elements and the characteristics of the voice types.

7. The system as claimed in claim 6, wherein the means for classifying the text elements according to the characteristics of the available voice types comprise means for identifying similar themes within the text elements and voice types.

8. The system as claimed in claim 6, wherein the means for classifying the text elements according to the characteristics of the available voice types comprise means for identifying similar intentions within the text elements and voice types.

9. A computer-readable storage, having stored thereon a computer program having a plurality of code sections executable by a machine for causing the machine to perform a method for automatically marking a document to be read by a text-to-speech reader with voice type identifiers, the method comprising the steps of:

identifying two or more voice types available to the text-to-speech reader, each voice type having a corresponding voice type identifier;

identifying text elements within the document, wherein identifying text elements comprises marking gross structural subdivisions of text with a first set of sequenced tags, marking individual paragraphs of the text with a second set of sequenced tags, and marking text elements with a third set of sequenced tags to generate a hierarchical tree identifying the text elements;

grouping similar text elements together, wherein the step of grouping comprises generating one or more clusters according to each identifiable topic of the document, syntactically parsing the document and subsequently performing text mining to determine which text elements in the document are similar, wherein similarity is based upon lexical affinities among the text elements;

classifying the grouped text elements according to voice types available to the text-to-speech reader; and

marking the classified grouped text elements within the document with corresponding voice type identifiers.

10. The computer-readable storage as claimed in claim 9, wherein the step of identifying text elements comprises breaking down the document into elements and code for separating out the text elements.

11. The computer-readable storage as claimed in claim 9, wherein the step of grouping similar text elements together comprises parsing for structural features of the text elements.

12. The computer-readable storage as claimed in claim 11, wherein the structural features of the text elements include at least one of the position of the text element in the document, the syntax of the text element, and text features within the text element.

13. The computer-readable storage as claimed in claim 11, wherein the step of grouping similar text elements further comprises parsing for thematic features of the text elements.

14. The computer-readable storage as claimed in claim 9, wherein the step of classifying the text elements according to the available voice types comprises finding the best match between the grouped text elements and the characteristics of the voice types.

15. The computer-readable storage as claimed in claim 14, wherein the step of classifying the text elements according to the characteristics of the available voice types comprises identifying similar themes within the text elements and voice types.

16. The computer-readable storage as claimed in claim 14, wherein the step of classifying the text elements according to the characteristics of the available voice types comprises identifying similar intentions within the text elements and voice types.