# US 12,183,328 B2 · Language model biasing system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,183,328 B2 - Document ID: US12183328B2 - Status: Granted - Kind: B2 - Application number: 18/318,495 - Filed: 2023-05-16 - Published: 2023-09-14 - Granted: 2024-12-31 - Earliest claimed: 2017-02-14 - Assignees: Google Inc. - Applicants: Google LLC - Inventors: Petar Aleksic; Pedro J. Moreno Mengibar - Web page: https://patentgrep.com/patent/US12183328B2 ## CPC - G10L 15/07: to the speaker - G10L 15/1815: {Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning} - G10L 15/187: Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams - G10L 15/197: Probabilistic grammars, e.g. word n-grams - G10L 15/01: Assessment or evaluation of speech recognition systems - G10L 15/30: Distributed recognition, e.g. in client-server systems, for mobile phones or network applications - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Methods, systems, and apparatus for receiving audio data corresponding to a user utterance and context data, identifying an initial set of one or more n-grams from the context data, generating an expanded set of one or more n-grams based on the initial set of n-grams, adjusting a language model based at least on the expanded set of n-grams, determining one or more speech recognition candidates for at least a portion of the user utterance using the adjusted language model, adjusting a score for a particular speech recognition candidate determined to be included in the expanded set of n-grams, determining a transcription of user utterance that includes at least one of the one or more speech recognition candidates, and providing the transcription of the user utterance for output. ## Claims ### Claim 1 A computer-implemented method when executed on data processing hardware that causes the data processing hardware to perform operations comprising: receiving an initial set of n-grams, the initial set of n-grams designated by an application developer of an application, each n-gram of the initial set of n-grams comprises n adjacent letters, symbols, or words; biasing, using the initial set of n-grams, a trained speech recognizer by modifying one or more parameters of a trained language model used by the trained speech recognizer, the one or more parameters modified to increase prediction probabilities associated with the trained speech recognizer recognizing n-grams in audio data that are included in the initial set of n-grams; receiving audio data corresponding to an utterance detected by a user device associated with a user; and processing, using the biased trained speech recognizer and the modified trained language model, the received audio data corresponding to the utterance to determine a transcription of the utterance that is biased toward the initial set of n-grams. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the initial set of n-grams are identified before the user speaks the utterance. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the operations further comprise: receiving context data for the user device, wherein receiving the initial set of n-grams comprises identifying the initial set of n-grams from the context data. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein: the context data comprises an application identifier associated with the application; and identifying the initial set of n-grams comprises retrieving data indicating one or more words or phrases corresponding to the application identifier. ### Claim 5 (depends on 3) The computer-implemented method of claim 3, wherein: the context data comprises a dialog state identifier; and identifying the initial set of n-grams comprises retrieving data indicating one or more words or phrases corresponding to the dialog state identifier. ### Claim 6 (depends on 3) The computer-implemented method of claim 3, wherein the context data comprises data that indicates a topic corresponding to an interface provided to the user by the user device. ### Claim 7 (depends on 3) The computer-implemented method of claim 3, wherein the context data comprises data indicating a task to be performed using an interface provided to the user by the user device. ### Claim 8 (depends on 3) The computer-implemented method of claim 3, wherein identifying the initial set of n-grams from the context data comprises: identifying a first set of one or more n-grams from the context data; and generating an expanded set of one or more n-grams based at least on the first set of n-grams, the expanded set of n-grams comprising one or more n-grams that are different from the n-grams in the first set of n-grams. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the utterance is detected by the user device providing an interface to the user. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein: the data processing hardware resides on the user device; or the data processing hardware resides on a server system in communication with the user device over a communication network. ### Claim 11 A system comprising: data processing hardware; and memory hardware in communication with the data processing hardware and storing instructions that, when executed on the data processing hardware, causes the data processing hardware to perform operations comprising: receiving an initial set of n-grams, the initial set of n-grams designated by an application developer of an application, each n-gram of the initial set of n-grams comprises n adjacent letters, symbols, or words; biasing, using the initial set of n-grams, a trained speech recognizer by modifying one or more parameters of a trained language model used by the trained speech recognizer, the one or more parameters modified to increase prediction probabilities associated with the trained speech recognizer recognizing n-grams in audio data that are included in the initial set of n-grams; receiving audio data corresponding to an utterance detected by a user device associated with a user; and processing, using the biased trained speech recognizer and the modified trained language model, the received audio data corresponding to the utterance to determine a transcription of the utterance that is biased toward the initial set of n-grams. ### Claim 12 (depends on 11) The system of claim 11, wherein the initial set of n-grams are identified before the user speaks the utterance. ### Claim 13 (depends on 11) The system of claim 11, wherein the operations further comprise: receiving context data for the user device, wherein receiving the initial set of n-grams comprises identifying the initial set of n-grams from the context data. ### Claim 14 (depends on 13) The system of claim 13, wherein: the context data comprises an application identifier associated with the application; and identifying the initial set of n-grams comprises retrieving data indicating one or more words or phrases corresponding to the application identifier. ### Claim 15 (depends on 13) The system of claim 13, wherein: the context data comprises a dialog state identifier; and identifying the initial set of n-grams comprises retrieving data indicating one or more words or phrases corresponding to the dialog state identifier. ### Claim 16 (depends on 13) The system of claim 13, wherein the context data comprises data that indicates a topic corresponding to an interface provided to the user by the user device. ### Claim 17 (depends on 13) The system of claim 13, wherein the context data comprises data indicating a task to be performed using an interface provided to the user by the user device. ### Claim 18 (depends on 13) The system of claim 13, wherein identifying the initial set of n-grams from the context data comprises: identifying a first set of one or more n-grams from the context data; and generating an expanded set of one or more n-grams based at least on the first set of n-grams, the expanded set of n-grams comprising one or more n-grams that are different from the n-grams in the first set of n-grams. ### Claim 19 (depends on 13) The system of claim 13, wherein the utterance is detected by the user device providing an interface to the user. ### Claim 20 (depends on 13) The system of claim 13, wherein: the data processing hardware resides on the user device; or the data processing hardware resides on a server system in communication with the user device over a communication network.