Google Inc.

Leonid Velikovich

Petar Aleksic

Pedro Jose Moreno Mengibar

Pedro J Moreno

Brian Strope

Michael Dennis Riley

Ágoston Weisz

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing contextual grammar selection are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance. The actions include generating a word lattice that includes multiple candidate transcriptions of the utterance and that includes transcription confidence scores. The actions include determining a context of the computing device. The actions include based on the context of the computing device, identifying grammars that correspond to the multiple candidate transcriptions. The actions include determining, for each of the multiple candidate transcriptions, grammar confidence scores that reflect a likelihood that a respective grammar is a match for a respective candidate transcription. The actions include selecting, from among the candidate transcriptions, a candidate transcription. The actions further include providing, for output, the selected candidate transcription as a transcription of the utterance.

Contextual tagging and biasing of grammars inside word lattices

A method includes receiving audio data corresponding to an utterance spoken by a user and processing, using a first recognition model, the audio data to generate a non-contextual candidate hypothesis as output from the first recognition model. The non-contextual candidate hypothesis has a corresponding likelihood score assigned by the first recognition model. The method also includes generating, using a second recognition model configured to receive personal context information, a contextual candidate hypothesis that includes a personal named entity. The method also includes scoring, based on the personal context information and the corresponding likelihood score assigned to the non-contextual candidate hypothesis, the contextual candidate hypothesis relative to the non-contextual candidate hypotheses. Based on the scoring of the contextual candidate hypothesis relative to the non-contextual candidate hypothesis, the method also includes generating a transcription of the utterance by selecting one of the contextual candidate hypothesis or the non-contextual candidate hypothesis.

Personalizable probabilistic models

A method includes receiving audio data corresponding to a query spoken and processing the audio data to generate multiple candidate hypotheses each represented by a respective sequence of hypothesized terms. For each candidate hypothesis, the method includes determining whether the sequence of hypothesized terms includes a source phrase from a list of phrase correction pairs. Each phrase correction pair includes a corresponding source phrase that was misrecognized and a corresponding target phrase replacing the source phrase. When the respective sequence of hypothesized terms includes the source phrase, the method includes generating a corresponding additional candidate hypothesis that replaces the source phrase. The method also includes ranking the multiple candidate hypotheses and each corresponding additional candidate hypothesis generated and generating a transcription of the query spoken by the user by selecting the highest ranking one of the multiple candidate hypotheses and each additional candidate hypothesis.

Lattice speech corrections

Speech processing techniques are disclosed that enable determining a text representation of named entities in captured audio data. Various implementations include determining the location of a carrier phrase in a word lattice representation of the captured audio data, where the carrier phrase provides an indication of a named entity. Additional or alternative implementations include matching a candidate named entity with the portion of the word lattice, and augmenting the word lattice with the matched candidate named entity.

Word lattice augmentation for automatic speech recognition

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for tagging during speech recognition. A word lattice that indicates probabilities for sequences of words in an utterance is obtained. A conditional probability transducer that indicates a frequency that sequences of both the words and semantic tags for the words appear is obtained. The word lattice and the conditional probability transducer are composed to construct a word lattice that indicates probabilities for sequences of both the words in the utterance and the semantic tags for the words. The word lattice that indicates probabilities for sequences of both the words in the utterance and the semantic tags for the words is used to generate a transcription that includes the words in the utterance and the semantic tags for the words.

Semantic model for tagging of word lattices

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for updating phonetic dictionaries. In one aspect, a method includes accessing a phonetic dictionary that identifies terms and one or more phonetic representations associated with each term, determining that a particular term that is identified in the phonetic dictionary is a spelling correction for another term that is identified in the phonetic dictionary, and storing, in the phonetic dictionary, one or more of the phonetic representations associated with the other term, with the particular term that is a spelling correction for the other term.

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New York, NY, United States of America

Leonid Velikovich