SYSTEM AND METHOD FOR TEXT-TO-PHONEME MAPPING WITH PRIOR KNOWLEDGE

• US 20070233490A1
• Filed: 04/03/2006
• Published: 10/04/2007
• Est. Priority Date: 04/03/2006
• Status: Abandoned Application

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Yao, Kaisheng
• Application Number:
  US11/278,497
• Publication Number:
  US 20070233490A1
• 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

A system for, and method of, text-to-phoneme (TTP) mapping and a digital signal processor (DSP) incorporating the system or the method. In one embodiment, the system includes: (1) a letter-to-phoneme (LTP) mapping generator configured to generate an LTP mapping by iteratively aligning a full training set with a set of correctly aligned entries based on statistics of phonemes and letters from the set of correctly aligned entries and redefining the full training set as a union of the set of correctly aligned entries and a set of incorrectly aligned entries created during the aligning and (2) a model trainer configured to update prior probabilities of LTP mappings generated by the LTP generator and evaluate whether the LTP mappings are suitable for training a decision-tree-based pronunciation model (DTPM).

Claims

1. A system for text-to-phoneme mapping, comprising:

a letter-to-phoneme mapping generator configured to generate a letter-to-phoneme mapping by iteratively aligning a full training set with a set of correctly aligned entries based on statistics of phonemes and letters from said set of correctly aligned entries and redefining said full training set as a union of said set of correctly aligned entries and a set of incorrectly aligned entries created during said aligning; and

a model trainer configured to update prior probabilities of letter-to-phoneme mappings generated by said letter-to-phoneme generator and evaluate whether said letter-to-phoneme mappings are suitable for training a decision-tree-based pronunciation model.

2. The system as recited in claim 1 wherein said letter-to-phoneme mapping generator is configured to employ an E-M-type algorithm iteratively to align said full training set with said set of correctly aligned entries.

3. The system as recited in claim 1 wherein said letter-to-phoneme mapping generator is configured to obtain said statistics by calculating a probability of a particular phoneme given a particular letter, calculating a probability of said particular letter given said particular phoneme and updating a posterior probability of said particular phoneme given said particular letter.

4. The system as recited in claim 1 wherein said letter-to-phoneme mapping generator is configured iteratively to align said full training set with said set of correctly aligned entries by text-to-phoneme aligning every entry in said training set to obtain a phoneme sequence having a maximum a posteriori probability and checking if every letter-phoneme pair in said every entry is allowed.

5. The system as recited in claim 1 wherein said model trainer is configured to evaluate whether said letter-to-phoneme mappings are suitable for training said decision-tree-based pronunciation model by pruning said letter-to-phoneme mappings generated by said letter-to-phoneme generator and comparing posterior probabilities in said letter-to-phoneme mappings to a threshold.

6. The system as recited in claim 1 wherein said letter-to-phoneme mapping generator is configured to generate said letter-to-phoneme mapping over a predetermined number of iterations and said model trainer is configured to evaluate a predetermined number of said letter-to-phoneme mappings.

7. The system as recited in claim 1 wherein said system is embodied in a digital signal processor.

8. A method of text-to-phoneme mapping, comprising:

generating a letter-to-phoneme mapping by iteratively aligning a full training set with a set of correctly aligned entries based on statistics of phonemes and letters from said set of correctly aligned entries and redefining said full training set as a union of said set of correctly aligned entries and a set of incorrectly aligned entries created during said aligning;

updating prior probabilities of letter-to-phoneme mappings generated by said letter-to-phoneme generator; and

evaluating whether said letter-to-phoneme mappings are suitable for training a decision-tree-based pronunciation model.

9. The method as recited in claim 8 wherein said generating comprises employing an E-M-type algorithm iteratively to align said full training set with said set of correctly aligned entries.

10. The method as recited in claim 8 wherein generating comprises obtaining said statistics by calculating a probability of a particular phoneme given a particular letter, calculating a probability of said particular letter given said particular phoneme and updating a posterior probability of said particular phoneme given said particular letter.

11. The method as recited in claim 8 wherein said aligning comprises aligning every entry in said training set to obtain a phoneme sequence having a maximum a posteriori probability and checking if every letter-phoneme pair in said every entry is allowed.

12. The method as recited in claim 8 wherein said evaluating comprises pruning said letter-to-phoneme mappings generated by said letter-to-phoneme generator and comparing posterior probabilities in said letter-to-phoneme mappings to a threshold.

13. The method as recited in claim 8 wherein said generating is carried out over a predetermined number of iterations and said evaluating is carried out on a predetermined number of said letter-to-phoneme mappings.

14. The method as recited in claim 8 wherein said method is carried out in a digital signal processor.

15. A digital signal processor, comprising:

data processing and storage circuitry controlled by a sequence of executable instructions configured to;

generate a letter-to-phoneme mapping by iteratively aligning a full training set with a set of correctly aligned entries based on statistics of phonemes and letters from said set of correctly aligned entries and redefining said full training set as a union of said set of correctly aligned entries and a set of incorrectly aligned entries created during said aligning;

update prior probabilities of letter-to-phoneme mappings generated by said letter-to-phoneme generator; and

evaluate whether said letter-to-phoneme mappings are suitable for training a decision-tree-based pronunciation model.

16. The digital signal processor as recited in claim 15 wherein said sequence of executable instructions is further configured to employ an E-M-type algorithm iteratively to align said full training set with said set of correctly aligned entries.

17. The digital signal processor as recited in claim 15 wherein said sequence of executable instructions is further configured to obtain said statistics by calculating a probability of a particular phoneme given a particular letter, calculating a probability of said particular letter given said particular phoneme and updating a posterior probability of said particular phoneme given said particular letter.

18. The digital signal processor as recited in claim 15 wherein said sequence of executable instructions is further configured to align every entry in said training set to obtain a phoneme sequence having a maximum a posteriori probability and check if every letter-phoneme pair in said every entry is allowed.

19. The digital signal processor as recited in claim 15 wherein said sequence of executable instructions is further configured to prune said letter-to-phoneme mappings generated by said letter-to-phoneme generator and compare posterior probabilities in said letter-to-phoneme mappings to a threshold.

20. The digital signal processor as recited in claim 15 wherein said sequence of executable instructions is further configured to generate said letter-to-phoneme mapping over a predetermined number of iterations and evaluate a predetermined number of said letter-to-phoneme mappings.