HMM-based text-to-phoneme parser and method for training same

• US 20030088416A1
• Filed: 11/06/2001
• Published: 05/08/2003
• Est. Priority Date: 11/06/2001
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: DSPC Technologies Limited
• Inventors: Griniasty, Meir
• Application Number:
  US10/013,239
• Publication Number:
  US 20030088416A1
• Time in Patent Office: Days
• US Class Current: 704/256
• 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
• CPC Class Codes: G10L 15/144 Training of HMMs

Abstract

An HMM-based text-to-phoneme parser uses probability information within a probability database to generate one or more phoneme strings for a written input word. Techniques for training the text-to-phoneme parser are provided.

Claims

1. A method for training a text-to-phoneme parser system, comprising:

generating first information based on pronunciations within a phonetic dictionary, said first information identifying a plurality of potential diphones;

pruning said plurality of potential diphones based on frequency of occurrence information to produce pruned diphones;

forming an extended set of phonemes that includes said pruned diphones as legal phonemes; and

generating second information, based on said extended set of phonemes, for use in performing text-to-phoneme parsing.

2. The method of claim 1, wherein:

said first information includes diphone emission information.

3. The method of claim 1, wherein:

said first information includes phoneme emission information.

4. The method of claim 1, wherein:

generating first information includes performing supervised segmentation of words within said phonetic dictionary.

5. The method of claim 4, wherein:

performing supervised segmentation includes performing a Viterbi search to identify an optimal segmentation for a first word based on a set of phonemes identified for said first word within said phonetic dictionary.

6. The method of claim 1, wherein:

generating first information includes performing cycles of supervised segmentation and probability generation for words within said phonetic dictionary.

7. The method of claim 1, wherein:

pruning said plurality of potential diphones includes selecting diphones from said plurality of potential diphones that have a highest number of occurrences.

8. The method of claim 1, wherein:

said phonetic dictionary identifies an initial set of phonemes; and

forming an extended set of phonemes includes adding said pruned diphones to said initial set of phonemes.

9. The method of claim 1, wherein:

generating second information includes generating phoneme emission probabilities for phonemes within said extended set of phonemes.

10. The method of claim 1, wherein:

generating second information includes generating phoneme transition probabilities for phonemes within said extended set of phonemes.

11. The method of claim 1, wherein:

generating second information includes generating a probability that a specific letter string will be induced given a present phoneme and a previous phoneme.

12. The method of claim 1, wherein:

generating second information includes generating a probability that a specific phoneme will be induced given a previous phoneme and a letter string emitted by said previous phoneme.

13. The method of claim 1, wherein:

generating second information includes performing supervised segmentation of words within said phonetic dictionary.

14. The method of claim 1, wherein:

generating second information includes performing cycles of supervised segmentation and probability generation for words within said phonetic dictionary.

15. A method for use in training a text-to-phoneme parser system, comprising:

segmenting words based on known word pronunciations to generate segmentation results;

generating probability information using said segmentations results, said probability information including a plurality of probability values;

identifying probability values within said probability information that are below a first threshold value; and

changing said identified probability values to a predetermined value.

16. The method of claim 15, wherein:

said predetermined value is said first threshold value.

17. The method of claim 15, further comprising:

re-segmenting said words, after changing said identified probability values, based on said probability information to generate new segmentation results.

18. The method of claim 17, further comprising:

generating new probability information using said new segmentations results, said new probability information including a plurality of probability values;

detecting probability values within said new probability information that are below a second threshold value; and

changing said detected probability values to a second predetermined value.

19. The method of claim 18, wherein:

said second threshold value is less than said first threshold value.

20. The method of claim 15, wherein:

said probability information includes phoneme emission probabilities.

21. The method of claim 15, wherein:

said probability information includes a probability that a specific letter string will be induced given a present phoneme and a previous phoneme.

22. The method of claim 15, wherein:

said probability information includes diphone emission probabilities, said diphone emission probabilities including a probability that a specific letter will be emitted by a given phoneme pair.

23. The method of claim 15, wherein:

said probability information includes phoneme transition probabilities.

24. The method of claim 23, wherein:

said phoneme transition probabilities include a probability that a specific phoneme will be induced given a previous phoneme.

25. The method of claim 23, wherein:

said phoneme transition probabilities include a probability that a specific phoneme will be induced given a previous phoneme and a letter string emitted by said previous phoneme.

26. The method of claim 23, wherein:

segmenting words includes segmenting words based on corresponding pronunciations within a phonetic dictionary.

27. A method for use in training a text-to-phoneme parser system, comprising:

segmenting words based on known word pronunciations to generate segmentation results; and

generating probability information using said segmentation results, said probability information including generalized transition probability information, said generalized transition probability information including a probability that a specific phoneme will be induced given a previous phoneme and a letter string emitted by said previous phoneme.

28. The method of claim 27, wherein:

said probability information includes generalized emission probability information, said generalized emission probability information including a probability that a specific letter string will be induced given a present phoneme and a previous phoneme.

29. The method of claim 27, wherein:

segmenting words includes segmenting words based on corresponding pronunciations within a phonetic dictionary.

30. The method of claim 27, wherein:

segmenting words includes identifying a best path through a Viterbi search table for a first word.

31. The method of claim 27, further comprising:

repeating segmenting words and generating probability information until a predetermined condition has been satisfied.

32. A text-to-phoneme parsing system, comprising:

a probability database including generalized transition probability information, said generalized transition probability information including a probability that a specific phoneme will occur given a previous phoneme and a letter string emitted by said previous phoneme, and

a text-to-phoneme parser to generate at least one phoneme string for a written input word based on information within said probability database.

33. The text-to-phoneme parsing system of claim 32, wherein:

said probability database includes generalized emission probability information, said generalized emission probability information including a probability that a specific letter string will be induced given a present phoneme and a previous phoneme.

34. The text-to-phoneme parsing system of claim 32, wherein:

said probability database includes probability information that was generated based upon word pronunciations within a phonetic dictionary.

35. The text-to-phoneme parsing system of claim 32, wherein:

said text-to-phoneme parser generates the N best phoneme strings for said written input word, where N is an integer greater than 1.