Google Inc.

Dmitry Osmakov

Gleb Skobeltsyn

Martin Baeuml

Evgeny A Cherepanov

Olga Kapralova

Behshad Behzadi

Alexandru Ovidiu Dovlecel

Carl-Anton Ingmarsson

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for speech recognition. One of the methods includes receiving first audio data corresponding to an utterance; obtaining a first transcription of the first audio data; receiving data indicating (i) a selection of one or more terms of the first transcription and (ii) one or more of replacement terms; determining that one or more of the replacement terms are classified as a correction of one or more of the selected terms; in response to determining that the one or more of the replacement terms are classified as a correction of the one or more of the selected terms, obtaining a first portion of the first audio data that corresponds to one or more terms of the first transcription; and using the first portion of the first audio data that is associated with the one or more terms of the first transcription to train an acoustic model for recognizing the one or more of the replacement terms.

Acoustic model training using corrected terms

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting follow-up queries to an initial transcription of an utterance. In some implementations, one or more follow-up queries that are pre-associated with a transcription of an initial utterance of a user are identified. A new or modified language model in which a respective probability associated with one or more of the follow-up queries is increased with respect to an initial language model is obtained. Subsequent audio data corresponding to a subsequent utterance of the user is then received. The subsequent audio data is processed using the new or modified language model to generate a transcription of the subsequent utterance. The transcription of the subsequent utterance is then provided for output to the user.

Follow-up voice query prediction

Methods, including computer programs encoded on a computer storage medium, for collaborative language model biasing. In one aspect, a method includes: obtaining (i) one or more initial candidate transcriptions, and (ii) one or more terms that are associated with a context; selecting one or more of the terms that are associated with the context, and that (i) do not occur in the candidate transcriptions, and (ii) are indicated as phonetically similar to one or more terms that do occur in the initial candidate transcriptions; generating one or more additional candidate transcriptions based on the (i) initial candidate transcriptions, and (ii) the selected terms; and providing the one or more additional candidate transcriptions to an automated speech recognizer.

Contextual improvement of voice query recognition

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Zurich, Switzerland

Dmitry Osmakov