System and method for spelling recognition using speech and non-speech input

1. A method for recognizing a combination of speech and alternate input, the method comprising:

receiving a non-speech input from a user;

dynamically constructing an unweighted grammar permitting all letter sequences that map to the received non-speech input;

constructing a weighted grammar using the unweighted grammar and a statistical letter model trained on domain data;

receiving speech from the user associated with the non-speech input, wherein receiving the speech occurs after receiving the non-speech input and after constructing the weighted grammar; and

recognizing the received speech and non-speech input using the constructed weighted grammar.

2. The method of claim 1 , wherein the statistical letter model is an N-gram letter model.

3. The method of claim 2 , wherein the N-gram letter model is unsmoothed.

4. The method of claim 1 , wherein the non-speech input is keypad input.

5. The method of claim 1 , wherein the non-speech input is received via a touch sensitive screen.

6. The method of claim 5 , wherein the non-speech input is received via a stylus on a touch-sensitive screen.

7. The method of claim 1 , further comprising generating a final letter string based on a database lookup.

8. A method of recognizing input from a user, the method comprising:

performing spelling recognition via automatic speech recognition (ASR) on received speech from a user, the ASR being performed using a statistical letter model trained on domain data and producing a letter lattice R LN ;

if an ASR confidence is below a predetermined level, then:

receiving non-speech input from the user;

generating a non-speech constraint grammar K; and

generating a letter string based on a composition of R LN and K.

9. The method of claim 8 , wherein the statistical letter model is an N-gram letter model.

10. The method of claim 8 , wherein the statistical letter model is unsmoothed.

11. The method of claim 8 , wherein the non-speech input is keypad input.

12. The method of claim 8 , wherein R LN and K are finite state transducers.

13. The method of claim 8 , wherein generating a letter string further comprises generating a letter string based on a composition of finite state transducers R LN , K and a transducer that eliminates silence and other filler words.

14. The method of claim 8 , further comprising generating a final letter string based on a database lookup.

15. The method of claim 14 , wherein generating the final letter string based on a database lookup further comprises using a finite state network that accepts only valid letter strings.

16. The method of claim 5 , wherein the database lookup is a database of valid names.

17. The method of claim 8 , wherein the received non-speech input only comprises a portion of a word or name.

18. The method of claim 8 , further comprising, if an ASR confidence is below a predetermined level, prompting the user to enter the first three or less letters of the input by using a keypad.

19. A system for recognizing a combination of speech and alternate input, the system comprising:

means for receiving a non-speech input from a user;

means for dynamically constructing an unweighted grammar permitting all letter sequences that map to the received non-speech input;

means for constructing a weighted grammar using the unweighted grammar and a statistical letter model trained on domain data;

means for receiving speech from the user associated with the non-speech input, wherein receiving the speech occurs after receiving the non-speech input and after constructing the weighted grammar; and

means for recognizing the received speech and non-speech input using the constructed weighted grammar.

20. A system for recognizing input from a user, the system comprising:

means for performing spelling recognition via automatic speech recognition (ASR) on received speech from a user, the ASR being performed using a statistical letter model trained on domain data and producing a letter lattice R LN ;

if an ASR confidence is below a predetermined level, then the means for performing spelling recognition further:

receives non-speech input from the user;

generates a non-speech constraint grammar K; and

generates a letter string based on a composition of R LN and K.

21. The system of claim 20 , wherein the statistical letter model is an N-gram letter model.

22. A computer-readable medium storing instructions for controlling a computing device to recognize a combination of speech and non-speech input, the instructions comprising:

receiving a non-speech input from a user;

dynamically constructing an unweighted grammar permitting all letter sequences that map to the received non-speech input;

constructing a weighted grammar using the unweighted grammar and a statistical letter model trained on domain data;

receiving speech from the user associated with the non-speech input, wherein receiving the speech occurs after receiving the non-speech input and after constructing the weighted grammar; and

recognizing the received speech and non-speech input using the constructed weighted grammar.

23. A computer-readable medium storing instructions for controlling a computing device to recognize input from a user, the instructions comprising:

performing spelling recognition via automatic speech recognition (ASR) on received speech from a user, the ASR being performed using a statistical letter model trained on domain data and producing a letter lattice R LN ;

if an ASR confidence is below a predetermined level, then:

receiving non-speech input from the user;

generating a non-speech constraint grammar K; and

generating a letter string based on a composition of R LN and K.