Google Inc.

Brian Christopher Milch

Alexander Mark Franz

Monika Hildegard Henzinger

Sergey Brin

Eric G Jackson

Benjamin Jay Diament

Jenny Zhou

A system may receive a voice search query and may determine word hypotheses for the voice query. Each word hypothesis may include one or more terms. The system may obtain a search query log and may determine, for each word hypothesis, a quantity of other search queries, in the search query log, that include the one or more terms. The system may determine weights based on the determined quantities. The system may generate, based on the weights, a first search query from the word hypotheses and may obtain a first set of search results. The system may modify, based on the first set of search results, one or more of the weights. The system may generate a second search query from the word hypotheses and obtain, based on the second search query, a second set of search results for the voice query.

Voice search engine interface for scoring search hypotheses

A system provides search results from a voice search query. The system receives a voice search query from a user, derives one or more recognition hypotheses, each being associated with a weight, from the voice search query, and constructs a weighted boolean query using the recognition hypotheses. The system then provides the weighted boolean query to a search system and provides the results of the search system to a user.

Voice interface for a search engine

A system receives a voice search query from a user, derives recognition hypotheses from the voice search query, and determines scores associated with the recognition hypotheses, the scores being based on a comparison of the recognition hypotheses to previously received search queries. The system discards at least one of the recognition hypotheses that is associated with a first score that is less than a threshold value, and constructs a first query using at least one non-discarded recognition hypothesis, where the at least one first non-discarded recognition hypothesis is associated with a second score that at least meets the threshold value. The system forwards the first query to a search system, receives first results associated with the first query, and provides the first results to the user.

A system and method for identifying compounds through iterative analysis of measure of association is disclosed. A limit on a number of tokens per compound is specified. Compounds within a text corpus are iteratively evaluated. A number of occurrences of one or more n-grams within the text corpus is determined. Each n-gram includes up to a maximum number of tokens, which are each provided in a vocabulary for the text corpus. At least one n-gram including a number of tokens equal to the limit based on the number of occurrences is identified. A measure of association between the tokens in the identified n-gram is determined. Each identified n-gram with a sufficient measure of association is added to the vocabulary as a compound token and the limit is adjusted.

System and method for identifying compounds through iterative analysis

A system and method for identifying language attributes through probabilistic analysis is described. A set of language classes and a plurality of training documents are defined, Each language class identifies a language and a character set encoding. Occurrences of one or more document properties within each training document are evaluated. For each language class, a probability for the document properties set conditioned on the occurrence of the language class is calculated. Byte occurrences within each training document are evaluated. For each language class, a probability for the byte occurrences conditioned on the occurrence of the language class is calculated.

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Mountain View, CA, United States of America

Brian Christopher Milch