Voice interface for a search engine

1. A method for providing search results, comprising:

receiving a voice search query from a user;

deriving one or more recognition hypotheses from the voice search query, each recognition hypothesis being associated with a weight and including one or more terms;

constructing a weighted boolean query using the recognition hypotheses, the constructing including:

determining a length of a shortest recognition hypothesis,

pruning a length of each recognition hypothesis up to the length of the shortest recognition hypothesis,

determining a length of a longest pruned recognition hypothesis,

selecting a number of recognition hypotheses based on one or more query parameters,

determining term weights, and

forming a weighted boolean query;

providing the weighted boolean query to a search system; and

providing results of the search system.

2. The method of claim 1 wherein the deriving one or more recognition hypotheses includes:

using one or more of a language model, phonetic dictionary, or acoustic models to derive the recognition hypotheses.

3. The method of claim 2 further comprising:

updating one or more of the language model, phonetic dictionary, or acoustic models using the voice search query.

4. The method of claim 1 further comprising:

identifying a language model based on at least one characteristic associated with the user, and

wherein the deriving one or more recognition hypotheses includes:

using the identified language model to derive the one or more recognition hypotheses.

5. The method of claim 1 wherein the query parameters include the determined length of the longest pruned recognition hypothesis, a value representing a total number of terms to be included in a query, and a value representing a proportion of new terms added from a first recognition hypothesis to a second recognition hypothesis.

6. The method of claim 1 wherein the query parameters vary by user or user group.

7. The method of claim 1 wherein the providing results of the search system includes:

adjusting a ranking of the results of the search system based on the weights.

8. The method of claim 1 wherein the providing results of the search system includes:

organizing the results based on the weights.

9. The method of claim 1 further comprising:

discarding, prior to constructing the weighted boolean query, those recognition hypotheses associated with a weight below a threshold value.

10. The method of claim 1 wherein the weighted boolean query is a weighted OR-query.

11. The method of claim 1 further comprising:

refining the weighted boolean query based on the results of the search system.

12. The method of claim 11 wherein the refining includes:

determining a quantity of results related to each recognition hypothesis, and

discarding recognition hypotheses having no results.

13. The method of claim 11 wherein the refining includes:

determining a quantity of results related to each recognition hypothesis, and

adjusting the weight associated with the recognition hypothesis based on the quantity.

14. The method of claim 1 further comprising:

detecting compounds in the one or more recognition hypotheses, and

wherein the constructing a weighted boolean query includes:

constructing the weighted boolean query using the recognition hypotheses and the detected compounds.

15. The method of claim 1 further comprising:

detecting compounds in the results of the search system;

refining the weighted boolean query based on the detected compounds;

providing the refined weighted boolean query to the search system; and

providing the new results.

16. A method for generating a search query, comprising:

receiving one or more recognition hypotheses, each recognition hypothesis being constructed from a voice search query;

determining a length of a shortest recognition hypothesis;

pruning a length of each recognition hypothesis up to the length of the shortest recognition hypothesis;

determining a length of a longest pruned recognition hypothesis;

selecting a number of recognition hypotheses based on the length of the longest pruned recognition hypothesis;

determining query term weights; and

forming a weighted boolean query out of each term position in the selected recognition hypotheses.

17. The method of claim 16 wherein the pruning includes:

removing noise words from the recognition hypotheses.

18. The method of claim 16 wherein the selecting includes:

identifying a number of recognition hypotheses based on the determined length of the longest pruned recognition hypothesis, a value representing a total number of terms to be included in a query, and a value representing a proportion of new terms added from a first recognition hypothesis to a second recognition hypothesis.

19. A server comprising:

a memory configured to store instructions; and

a processor configured to execute the instructions to receive one or more recognition hypothesis, each recognition hypothesis being constructed from a voice search query, determine a length of a shortest recognition hypothesis, prune a length of each recognition hypothesis up to the length of the shortest recognition hypothesis, determine a length of a longest pruned recognition hypothesis, select a number of recognition hypotheses, the number being based on a value representing the length of the longest pruned recognition hypothesis, determine query term weights, and form a weighted boolean query out of each term position in the selected recognition hypotheses.

20. A computer-readable medium containing instructions for controlling at least one processor to perform a method for generating a search query, comprising:

receiving at least one recognition hypothesis, the recognition hypothesis being constructed from a voice search query and having one or more terms;

determining a length of a shortest recognition hypothesis;

pruning a length of each recognition hypothesis up to the length of the shortest recognition hypothesis;

determining a length of a longest pruned recognition hypothesis;

selecting a number of recognition hypotheses, the number being based on the length of the longest pruned recognition hypothesis;

determining term weights; and

forming a weighted boolean query out of the selected recognition hypotheses.