Processing speech to text queries by optimizing conversion of speech queries to text

1. A method for improving an accuracy of a generated text query based upon a phrase of an audible natural language query, wherein the method is performed on a computing system and comprises:

receiving a plurality of speech to text translation alternatives for the phrase of the audible natural language query;

comparing each of the translation alternatives to elements of a static analysis of a known domain including at least one of word lists of the known domain, user matrices of the known domain, and facets, each of which having a limited number of possible values;

identifying a respective term in at least one translation alternative from the plurality of translation alternatives based on the comparing;

substituting, for the identified term in each of the at least one translation alternative, a respective known name from the known domain based on a distance measure between the identified term and a closed list of names from the known domain;

tagging and parsing each of the translation alternatives with generic tags and specific tags for the known domain that is at least partially structured, wherein the tagging and parsing further comprises generating one or more new tags based on previous findings to build up complex expressions incrementally and create new structures from primitive structures, and the tagging and parsing is based on:

the static analysis of the known domain which determines known relationships between known values of the known domain,

known tags of the known domain, and

custom rules, each of which includes a respective name of an action to invoke and one or more respective conditions that trigger the action, wherein the one or more generated new tags are available upon creation for use by subsequent rules;

identifying a particular translation alternative from the translation alternatives having a section of the phrase that is not tagged and covered by the tagging and parsing;

correcting an error due to speech to text translation and associated with the section that is not covered by replacing one or more terms of the section based on a known list of common errors associated with the known domain;

ranking the translation alternatives based on the tagging and parsing; and

generating a text query to perform the natural language query by translating the phrase of the natural language query to a machine readable statement based on the ranking, wherein:

the tagging and parsing, the substituting, for each of the identified term in each of the at least one translation alternative, the respective known name from the known domain, the correcting an error due to speech to text translation and the ranking of the translation alternatives improve an accuracy of the generated text query.

2. The method of claim 1 , further comprising confirming the corrected error by resubmitting the corrected error for subsequent tagging and parsing, wherein the corrected error is confirmed when the subsequent tagging and parsing successfully tags and parses the corrected error.

3. The method of claim 1 , wherein the ranking of the translation alternatives is based on identifying translation alternatives that overlap with the limited number of possible values of one of the facets.

4. The method of claim 1 , further comprising refining the translation alternatives before tagging and parsing by correcting misspellings and errors within the translation alternatives to words or phrases that exist in the known domain.

5. The method of claim 4 , wherein correcting misspellings and errors in names within the translation alternatives is based on a user matrix that defines known names within the known domain and user relationships between the known names, wherein the relationships include measures of closeness between the known names, the measures of closeness being determined based on a frequency of interactions between two particular known names in the known domain.

6. The method of claim 5 , further comprising:

tagging and parsing the translation alternatives based on the measures of closeness, so that the translation alternatives intended for a particular known name will be ranked higher when the translation alternatives include one of the known names that has a high measure of closeness with the particular known name.