Generating large units of graphonemes with mutual information criterion for letter to sound conversion

• US 20050203739A1
• Filed: 03/10/2004
• Published: 09/15/2005
• Est. Priority Date: 03/10/2004
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Jiang, Li, Hwang, Mei-Yuh
• Granted Patent: US 7,693,715 B2
• Time in Patent Office: Days
• US Class Current: 704/254
• 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

A method and apparatus are provided for segmenting words into component parts. Under the invention, mutual information scores for pairs of graphoneme units found in a set of words are determined. Each graphoneme unit includes at least one letter. The graphoneme units of one pair of graphoneme units are combined based on the mutual information score. This forms a new graphoneme unit. Under one aspect of the invention, a syllable n-gram model is trained based on words that have been segmented into syllables using mutual information. The syllable n-gram model is used to segment a phonetic representation of a new word into syllables. Similarly, an inventory of morphemes is formed using mutual information and a morpheme n-gram is trained that can be used to segment a new word into a sequence of morphemes.

Claims

1. A method of segmenting words into component parts, the method comprising:

determining mutual information scores for graphoneme units, each graphoneme unit comprising at least on letter in the spelling of a word;

using the mutual information scores to combine graphoneme units into a larger graphoneme unit; and

segmenting words into component parts to form a sequence of graphonemes.

2. The method of claim 1 wherein combining graphonemes comprises combining the letters of each graphoneme to produce a sequence of letters for the larger graphoneme unit and combining the phones of each graphoneme to produce a sequence of phones for the larger graphoneme unit.

3. The method of claim 1 further comprising using the segmented words to generate a model.

4. The method of claim 3 wherein the model describes the probability of a graphoneme unit given a context within a word.

5. The method of claim 4 further comprising using the model to determine a pronunciation of a word given the spelling of the word.

6. The method of claim 1 wherein using the mutual information scores comprises summing at least two mutual information scores determined for a single larger graphoneme unit to form a strength.

7. A computer-readable medium having computer-executable instructions for performing steps comprising:

determining mutual information scores for pairs of graphoneme units found in a set of words, each graphoneme unit comprising at least one letter;

combining the graphoneme units of one pair of graphonome units to form a new graphoneme unit based on the mutual information scores; and

identifying a set of graphoneme units for a word based in part on the new graphoneme unit.

8. The computer-readable medium of claim 7 wherein combining the graphoneme units comprises combining the letters of the graphoneme units to form a sequence of letters for the new graphoneme unit.

9. The computer-readable medium of claim 8 wherein combining the graphoneme units further comprises combining the phones of the graphoneme units to form a sequence of phones for the new gaphoneme unit.

10. The computer-readable medium of claim 7 further comprising identifying a set of graphonemes for each word in a dictionary.

11. The computer-readable medium of claim 10 further comprising using the sets of graphonemes identified for the words in the dictionary to train a model.

12. The computer-readable medium of claim 11 wherein the model describes the probability of a graphoneme unit appearing in a word.

13. The computer-readable medium of claim 12 wherein the probability is based on at least one other graphoneme unit in the word.

14. The computer-readable medium of claim 11 further comprising using the model to determine a pronunciation for a word given the spelling of the word.

15. The computer-readable medium of claim 7 wherein combining graphoneme units based on the mutual information score comprises summing at least two mutual information scores associated with a new graphoneme unit.

16. A method of segmenting a word into syllables, the method comprising:

segmenting a set of words into phonetic syllables using mutual information scores;

using the segmented set of words to train a syllable n-gram model; and

using the syllable n-gram model to segment a phonetic representation of a word into syllables via forced alignment.

17. A method of segmenting a word into morphemes, the method comprising:

segmenting a set of words into morphemes using mutual information scores;

using the segmented set of words to train a morpheme n-gram model; and

using the morpheme n-gram model to segment a word into morphemes via forced alignment.