Method and system for retrieving documents with spoken queries

• US 20050149516A1
• Filed: 02/25/2005
• Published: 07/07/2005
• Est. Priority Date: 04/25/2002
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Ramakrishnan, Bhiksha, Woelfel, Joseph K., Wolf, Peter P.
• Granted Patent: US 7,542,966 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/3343 using phonetics
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

A method indexes and retrieves documents stored in a database. A document feature vector is extracted from each document and the documents are then indexed according to the feature vectors. A spoken query is converted to an intermediate representation representing likelihoods of possible sequential combinations of terms in the spoken query. A query certainty vector is generated from the intermediate representation. Other information is acquired. The other information is combined with the query certainty vector. The query vector and the other information are then compared to each of the document feature vectors to retrieve a ranked result set of documents.

Claims

1. A computer implemented method for indexing and retrieving documents stored in a database, comprising the steps of:

extracting a document feature vector from each of a plurality of documents;

indexing each of the plurality of documents according the associated document feature vector;

converting a spoken query to an intermediate representation representing possible sequential combinations of terms in the spoken query;

generating a query certainty vector from the intermediate representation;

acquiring other information;

combining the other information with the query certainty vector; and

comparing the query vector and the other information to each of the document feature vectors to retrieve a ranked result set of documents.

3. The method of claim 1, in which the intermediate representation is a lattice of terms.

4. The method of claim 1, in which the terms are words.

5. The method of claim 1, in which the terms are parts of words.

6. The method of claim 1, in which the intermediate representation is an n-best list of terms.

7. The method of claim 1, in which the terms are phonemes.

8. The method of claim 1, in which the documents describe points of interest, each point of interest having a location, and further comprising:

determining a position of a user generating the spoken query;

determining a distance between the position of the user and each point of interest matching the spoken query to produce the other information; and

ranking the set of result documents according to the distances.

9. The method of claim 1, further comprising:

converting a plurality of spoken queries to a plurality of an intermediate representations, the plurality of spoken queries linked by logical operators;

generating a plurality of query certainty vectors from the plurality of query certainty vectors to produce the other information; and

comparing the plurality query vector and the other information to each of the document feature vectors according to the logical operators to retrieve the ranked result set of documents.

10. The method of claim 1, further comprising:

converting a spoken preference to a corresponding intermediate representation;

a preference vector from the corresponding intermediate representation to produce the other information; and

ranking the set of result documents according to the preference vector

11. The method of claim 10, in which the preference vector is weighted by weights, and further comprising:

multiplying the query vector by the preference vector.

12. The method of claim 11, in which weights can be positive and negative, and further comprising:

comparing the query vector and the preference vector to each of the document feature vectors to retrieve the ranked result set of documents.

2. The method of claim 2, further comprising:

projecting the document feature vector and the query certainty vector to a low dimension.

13. A system for indexing and retrieving documents, comprising:

a plurality of documents, each document having an associated document feature vector;

a database indexing each of the plurality of documents according the associated document feature vector;

a speech recognition engine converting a spoken query to an intermediate representation representing possible sequential combinations of terms in the spoken query;

means for generating a query certainty vector from the intermediate representation;

means for acquiring other information;

means for combining the other information with the query certainty vector; and

a comparator configured to compare the query vector and the other information to each of the document feature vectors to retrieve a ranked result set of documents.