Method and apparatus for preparing a document to be read by text-to-speech reader
First Claim
1. A system for automatically marking a document to be read by a text-to-speech reader with voice type identifiers, said system comprising:
- at least one processor programmed to;
identify two or more voice types available to the text-to-speech reader, each voice type having a corresponding voice type identifier;
identify text elements within the document by 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;
group similar text elements together 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;
classify the grouped text elements according to voice types available to the text-to-speech reader; and
mark the classified grouped text elements within the document with corresponding voice type identifiers.
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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.
33 Citations
16 Claims
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1. A system for automatically marking a document to be read by a text-to-speech reader with voice type identifiers, said system comprising:
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at least one processor programmed to; identify two or more voice types available to the text-to-speech reader, each voice type having a corresponding voice type identifier; identify text elements within the document by 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; group similar text elements together 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; classify the grouped text elements according to voice types available to the text-to-speech reader; and mark the classified grouped text elements within the document with corresponding voice type identifiers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer-readable storage medium, encoded with computer program instructions that, when executed by a machine, cause 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:
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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 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. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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Specification