Text-to-speech system and a method and apparatus for training the same based upon intonational feature annotations of input text

Claim 2

A machine implemented method of training a system for converting between text and speech, said method comprising the steps of (a) annotating a set of predetermined text with intonational feature annotations to generate annotated text, said set of predetermined text and said annotated text having a physically tangible readable form; (b) generating a set of structural information regarding said set of predetermined text; (c) generating a statistical representation of intonational feature information, the statistical representation being a function of said set of structural information and said intonational feature annotations; and (d) storing said statistical representation in said system for use by said system in converting between speech and text.

Claim 3

The method of claim 1 wherein the step of annotating comprises prosodically annotating the set of predetermined text with expected intonational features.

Claim 4

The method of claim 1 wherein the system is a text-to-speech system.

Claim 5

The method of claim 3 wherein the intonational feature annotations comprise intonational phrase boundaries.

Claim 6

The method of claim 1 wherein generating a statistical representation comprises generating a set of decision nodes.

Claim 7

The method of claim 5 wherein generating the set of decision nodes comprises generating a hidden Markov model.

Claim 8

The method of claim 5 wherein generating the set of decision nodes comprises generating a neural network.

Claim 9

The method of claim 5 wherein generating the set of decision nodes comprises performing classification and regression tree techniques.

Claim 10

The method of claim 1 wherein the steps (a) to (c) are performed on a computer.

Claim 11

The method of claim 1 wherein the step of generating a statistical representation of intonational feature information is performed on a phrasing module.

Claim 14

The apparatus of claim 11 further comprising: (a) a post processor for processing the output of said phrasing module to generate a synthesized speech signal; and (b) means for applying the synthesized speech signal to an acoustic output device.

Claim 15

The apparatus of claim 11 wherein the stored statistical representation comprises a decision tree.

Claim 16

The apparatus of claim 11 wherein the stored statistical representation comprises a hidden Markov model.

Claim 17

The apparatus of claim 11 wherein the stored statistical representation comprises a neural network.

Claim 18

The apparatus of claim 11 wherein said phrasing module comprises a generator, said generator answering a set of stored queries regarding the set of input text, the set of input text comprising a current sentence, the current sentence comprising a beginning, an end, and a plurality of words, each word in the plurality of words being a part of at least one set of words, w i and w j , wherein w i and w j each comprise at least one syllable and each have a part of speech associated therewith and each have a potential noun phrase associated therewith, the potential noun phrase having a beginning and an end, and further wherein w i and w j represent real words to the left and right, respectively, of a potential intonational phrase boundary site, <w i and w j >, and wherein w i-1 and w j+1 represent real words to the left and right, respectively of w i and w j , the set of stored queries comprising at least one query selected from the group consisting of (a) is w i intonationally prominent and if not, is it further reduced?; (b) is w j intonationally prominent and if not, is it further reduced?; (c) what is the part of speech of w i ?; (d) what is the part of speech of w i-1 ,?; (e) what is the part of speech of w j ?; (f) what is the part of speech of w j+1 ?; (g) how many words are in the current sentence?; (h) what is the distance, in real words, from w j to the beginning of the sentence?; (i) what is the distance, in real words, from w j to the end of the sentence?; (j) what is the location of the potential intonational boundary site with respect to the nearest noun phrase?; (k) if the potential intonational boundary site is within a noun phrase, how far is it from the beginning of the noun phrase?; (l) what is the size, in real words, of the current noun phrase?; (m) how far into the noun phrase is w i ?; (n) how many syllables precede the potential intonational boundary site in the current sentence?; (o) how many lexically stressed syllables precede the potential intonational boundary site in the current sentence?; (p) what is the total number of strong syllables in the current sentence?; (q) what is the stress level of the syllable immediately preceding the potential intonational boundary site?; (r) what is the result when one divides the distance from w j to the last intonational boundary assigned by the total length of the last intonational phrase?; (s) is there punctuation at the potential intonational boundary site?; and (t) how many primary or secondary stressed syllables exist between the potential intonational boundary site and the beginning of the current sentence.

Claim 19

A machine implemented method of converting text to speech said method comprising: (a) accessing a stored statistical representation from a phrasing module, the stored statistical representation being a function of a set of predetermined text and intonational feature annotations therefor; and (b) applying a set of input text having a physically tangible readable form to the stored statistical representation to generate an output representative of the set of input text.

Claim 20

A machine implemented method of converting text to speech said method comprising: (a) accessing a stored statistical representation from a phrasing module, the stored statistical representation being a function of a set of predetermined text and intonational feature annotations therefor; and (b) applying a set of input text having a physically tangible readable form to the stored statistical representation to generate an output representative of the set of input text.

Claim 21

The method of claim 17 further comprising: (a) post-processing the output to generate a synthesized speech signal; and (b) applying the synthesized speech signal to an acoustic output device.

Claim 22

The method of claim 17 wherein the stored statistical representation comprises a decision tree.

Claim 23

The method of claim 17 wherein the stored statistical representation comprises a hidden Markov model.

Claim 24

The method of claim 17 wherein the stored statistical representation comprises a neural network.

Claim 25

The method of claim 17 wherein the step of applying comprises answering a set of stored queries regarding the set of input text, the set of input text comprising a current sentence, the current sentence comprising a beginning, an end, and a plurality of words, each word in the plurality of words being a part of at least one set of words, w i and w j , wherein w i and w j each comprise at least one syllable and each have a part of speech associated therewith and each have a potential noun phrase associated therewith, the potential noun phrase having a beginning and an end, and further wherein w i and w j represent real words to the left and right, respectively, of a potential intonational phrase boundary site, <w i and w j >, and wherein w i-1 and w j-1 represent real words to the left and right, respectively of w i and w, the set of stored queries comprising at least one query selected from the group consisting of: (a) is w i intonationally prominent and if not, is it further reduced?; (b) is w j intonationally prominent and if not, is it further reduced?; (c) what is the part of speech of w i ?; (d) what is the part of speech of w i-1 ?; (e) what is the part of speech of w j ?; (f) what is the part of speech of w j+1 ?; (g) how many words are in the current sentence?; (h) what is the distance, in real words, from w j to the beginning of the sentence?; (i) what is the distance, in real words, from w j to the end of the sentence?; (j) what is the location of the potential intonational boundary site with respect to the nearest noun phrase?; (k) if the potential intonational boundary site is within a noun phrase, how far is it from the beginning of the noun phrase?; (l) what is the size, in real words, of the current noun phrase?; (m) how far into the noun phrase is w i ?; (n) how many syllables precede the potential intonational boundary site in the current sentence?; (o) how many lexically stressed syllables precede the potential intonational boundary site in the current sentence?; (p) what is the total number of strong syllables in the current sentence?; (q) what is the stress level of the syllable immediately preceding the potential intonational boundary site?; (r) what is the result when one divides the distance from w j to the last intonational boundary assigned by the total length of the last intonational phrase?; (s) is there punctuation at the potential intonational boundary site?; and (t) how many primary or secondary stressed syllables exist between the potential intonational boundary site and the beginning of the current sentence.

Claim 26

The method of claim 17 wherein said output is stored on a computer.

Claim 27

A machine implemented method of training a text-to-speech system, said method comprising the steps of: generating a statistical representation, said statistical representation being a function of a set of structural information of a set of text and a set of intonational feature annotations of an annotated version of said set of text; and storing said statistical representation on a text-to-speech system for use ill generating an intonational phrased output for future text input into the system.

Claim 28

A machine implemented method of training a text-to-speech system, said method comprising the steps of: generating a statistical representation, said statistical representation being a function of a set of structural information of a set of text and a set of intonational feature annotations of an annotated version of said set of text; and storing said statistical representation on a text-to-speech system for use ill generating an intonational phrased output for future text input into the system.

Claim 35

A machine implemented method of converting text to speech, said method comprising: (a) accessing a stored statistical representation from a phrasing module, the stored statistical representation being a function of a set of predetermined text and intonational feature annotations therefor; (b) applying a set of input text having a physically tangible readable form to the stored statistical representation to generate an output representative of the set of input text; and (c) post-processing the output to generate a synthesized speech signal.

Claim 36

A machine implemented method of converting text to speech, said method comprising: (a) accessing a stored statistical representation from a phrasing module, the stored statistical representation being a function of a set of predetermined text and intonational feature annotations therefor; (b) applying a set of input text having a physically tangible readable form to the stored statistical representation to generate an output representative of the set of input text; and (c) post-processing the output to generate a synthesized speech signal.