Google Inc.

Martin Jansche

Pedro Jose Moreno Mengibar

Ciprian Ioan Chelba

Matthew I Lloyd

Michael Dennis Riley

Fadi Biadsy

Mark Edward Epstein

Andrew M Rosenberg

Terry Tai

Sascha Benjamin Brawer

Richard Sproat

Hiroshi Takenaka

Yui Terashima

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transforming text strings. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving input string having a plurality of terms, the input string being in a first form; transforming the input string from the first form to a second form including: applying one or more rules to the input string to identify one or more terms for translation, the one or more identified terms being fewer than the plurality of terms, translating the identified one or more terms to one or more translated terms in the second form, and transliterating the remaining terms of the plurality of terms into transliterated terms in the second form; and concatenating the translated and transliterated terms to form a hybrid output string in the second form.

Conversion of input text strings

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for building language models. One of the methods includes identifying a first group of one or more users associated with a user in a social network. The method includes identifying first linguistic information associated with the first group. The method includes generating a first language model based on the first linguistic information. The method includes identifying a second group of one or more users associated with the user. The method includes identifying second linguistic information associated with the second group. The method includes generating a second language model based on the second linguistic information. The method includes associating the first language model and the second language model with the user.

Building language models for a user in a social network from linguistic information

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for acoustic model generation. One of the methods includes identifying one or more demographic characteristics for a user of a social networking site. The method includes receiving speech data from the user, the speech data associated with a user device. The method includes storing the speech data associated with demographic characteristics of the user and the user device.

Using social networks to improve acoustic models

The subject matter of this specification can be implemented in a computer-implemented method that includes receiving utterances and transcripts thereof. The method includes analyzing the utterances and transcripts to determine certain attributes, such as distances between prosodic contours for pairs of utterances. A model can be generated that can be used to estimate a distance between a determined prosodic contour for a received utterance and an unknown prosodic contour for a synthesized utterance when given a distance between attributes for text associated with the received utterance and the synthesized utterance.

Generating prosodic contours for synthesized speech

The present disclosure includes processing a signal to generate a first sub-set of data, transmitting the first sub-set of data for generation of a reconstructed audio signal, the reconstructed audio signal having a fidelity relative to the signal, processing the signal to generate a second sub-set of data and a third sub-set of data, the second sub-set of data defining a second portion of the signal and comprising data that is different than data of the first sub-set of data, and the third sub-set of data defining a third portion of the signal and comprising data that is different than data of the first and second sub-sets of data, comparing a priority of the second sub-set of data to a priority of the third sub-set of data, and transmitting one of the second sub-set of data and the third sub-set of data over the network for improving the fidelity.

Progressive encoding of audio

Disclosed embodiments relate to mapping an utterance to an action using a classifier. One illustrative computing device includes a user interface having an input component. The computing device further includes a processor and a computer-readable storage medium, having stored thereon program instructions that, upon execution by the processor, cause the computing device to perform a set of operations including: receiving an audio utterance via the input component; determining a text string based on the utterance; determining a string-feature vector based on the text string; selecting a target classifier from a set of classifiers, wherein the target classifier is selected based on a determination that a string-feature criteria of the target classifier corresponds to at least one string-feature of the string-feature vector; and initiating a target action that corresponds to the target classifier.

Mapping an audio utterance to an action using a classifier

Methods and systems for log mining for grammar-based text processing are provided. A method may comprise receiving, from a device, an activity log. The activity log may comprise one or more of an input instruction, a determined function based at least in part on a match of the input instruction to a grammar-based textual pattern including associations of a given function based on one or more grammars, and a response determination based on an acknowledgement of the determined function. The method may also comprise comparing at least a portion of the activity log with stored activity logs in order to determine a correlation between the activity log and the stored activity logs. The method may also comprise modifying the grammar-based textual pattern based on the determined correlation and providing information indicative of the modification to the device so as to update the grammar-based textual pattern.

Log mining to modify grammar-based text processing

The subject matter of this specification can be implemented in, among other things, a computer-implemented method including receiving text to be synthesized as a spoken utterance. The method includes analyzing the received text to determine attributes of the received text and selecting one or more utterances from a database based on a comparison between the attributes of the received text and attributes of text representing the stored utterances. The method includes determining, for each utterance, a distance between a contour of the utterance and a hypothetical contour of the spoken utterance, the determination based on a model that relates distances between pairs of contours of the utterances to relationships between attributes of text for the pairs. The method includes selecting a final utterance having a contour with a closest distance to the hypothetical contour and generating a contour for the received text based on the contour of the final utterance.

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New York, NY, United States of America

Martin Jansche