Query rewrite corrections

• US 9,514,743 B2
• Filed: 07/29/2015
• Issued: 12/06/2016
• Est. Priority Date: 08/29/2014
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Skobeltsyn, Gleb, Cherepanov, Evgeny A., Behzadi, Behshad
• Primary Examiner(s): Baker, Charlotte M
• Application Number:
  US14/812,811
• Publication Number:
  US 20160063994A1
• Time in Patent Office: 496 Days
• Field of Search: 704/257, 704/231, 704/252, 704/236, 704/251, 704/2, 704/3, 704/4, 704/6, 704/7, 704/8, 704/260, 704/267, 704/275, 704/277
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/685 using automatically derived transcript of audio data, e.g. lyrics speech recognition G10L15/00
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G10L 15/01 Assessment or evaluation of speech recognition systems
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/187 Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for natural language processing. One of the methods includes receiving a first voice query; generating a first recognition output; receiving a second voice query; determining from a recognition of the second voice query that the second voice query triggers a correction request; using the first recognition output and the second recognition to determine a plurality of candidate corrections; scoring each candidate correction; and generating a corrected recognition output for a particular candidate correction having a score that satisfies a threshold value.

Claims

1. A method comprising:

receiving a first voice query;

generating a first recognition output;

receiving a second voice query;

determining from a recognition of the second voice query that the second voice query triggers a correction request, wherein determining that the recognition of the second voice query triggers the correction request includes parsing the second voice query to detect a correction prefix;

using the first recognition output and the second recognition to determine a plurality of candidate corrections;

scoring each candidate correction; and

generating a corrected recognition output for a particular candidate correction having a score that satisfies a threshold value.

2. The method of claim 1, wherein using the first recognition output and the second recognition to determine a plurality of candidate corrected queries comprises:

determining a misrecognition portion of the first recognition output; and

substituting the misrecognition portion with one or more candidate n-grams to form a candidate correction.

3. The method of claim 2, wherein the one or more candidate n-grams are obtained from one or more of:

recognition of a correction portion of the second voice query;

generated hypotheses of the correction portion of the second voice query;

a misrecognition index of phonetically similar n-grams;

or

n-grams associated with a type or category associated with the correction portion of the second voice query.

4. The method of claim 1, wherein each candidate corrected query is scored based at least in part on a phonetic distance between the candidate correction and the first voice input.

5. The method of claim 1, wherein each candidate corrected query is scored based at least in part on a query popularity of the candidate correction.

6. The method of claim 5, wherein the query popularity is based on query counts in a query log that match the candidate correction.

7. The method of claim 5, wherein the query popularity is based at least on a change in query popularity between the first voice query and the candidate correction.

8. One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

receiving a first voice query;

generating a first recognition output;

receiving a second voice query;

determining from a recognition of the second voice query that the second voice query triggers a correction request, wherein determining that the recognition of the second voice query triggers the correction request includes parsing the second voice query to detect a correction prefix;

using the first recognition output and the second recognition to determine a plurality of candidate corrections;

scoring each candidate correction; and

generating a corrected recognition output for a particular candidate correction having a score that satisfies a threshold value.

9. A system comprising:

a user device; and

one or more computers configured to interact with the user device and to perform operations comprising;

receiving a first voice query from the user device;

generating a first recognition output;

receiving a second voice query from the user device;

determining from a recognition of the second voice query that the second voice query triggers a correction request, wherein determining that the recognition of the second voice query triggers the correction request includes parsing the second voice query to detect a correction prefix;

using the first recognition output and the second recognition to determine a plurality of candidate corrections;

scoring each candidate correction; and

generating a corrected recognition output for a particular candidate correction having a score that satisfies a threshold value.

10. The system of claim 9, wherein using the first recognition output and the second recognition to determine a plurality of candidate corrected queries comprises:

determining a misrecognition portion of the first recognition output; and

substituting the misrecognition portion with one or more candidate n-grams to form a candidate correction.

11. The system of claim 10, wherein the one or more candidate n-grams are obtained from one or more of:

recognition of a correction portion of the second voice query;

generated hypotheses of the correction portion of the second voice query;

a misrecognition index of phonetically similar n-grams;

or

n-grams associated with a type or category associated with the correction portion of the second voice query.

12. The system of claim 9, wherein each candidate corrected query is scored based at least in part on a phonetic distance between the candidate correction and the first voice input.

13. The system of claim 9, wherein each candidate corrected query is scored based at least in part on a query popularity of the candidate correction.

14. The system of claim 13, wherein the query popularity is based on query counts in a query log that match the candidate correction.

15. The system of claim 13, wherein the query popularity is based at least on a change in query popularity between the first voice query and the candidate correction.