# US 8,571,861 B2 · System and method for processing speech recognition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,571,861 B2 - Document ID: US8571861B2 - Status: Granted - Kind: B2 - Application number: 13/690,636 - Filed: 2012-11-30 - Published: 2013-04-11 - Granted: 2013-10-29 - Earliest claimed: 2000-10-13 - Assignees: AT&T Intellectual Property II, L.P. - Applicants: AT&T Intellectual Property II, L.P. - Inventors: Richard C. Rose; Sarangarajan Parthasarathy; Aaron Edward Rosenberg; Shrikanth Sambasivan Narayanan - Web page: https://patentgrep.com/patent/US8571861B2 ## CPC - G10L 15/04: Segmentation; Word boundary detection - G10L 15/08: Speech classification or search - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G10L 15/063: {Training} - G10L 15/197: Probabilistic grammars, e.g. word n-grams - G10L 2015/0631: {Creating reference templates; Clustering} - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract An automatic speech recognition (ASR) system and method is provided for controlling the recognition of speech utterances generated by an end user operating a communications device. The ASR system and method can be used with a mobile device that is used in a communications network. The ASR system can be used for ASR of speech utterances input into a mobile device, to perform compensating techniques using at least one characteristic and for updating an ASR speech recognizer associated with the ASR system by determined and using a background noise value and a distortion value that is based on the features of the mobile device. The ASR system can be used to augment a limited data input capability of a mobile device, for example, caused by limited input devices physically located on the mobile device. ## Claims ### Claim 1 A method comprising: receiving a speech utterance from a client device, the speech utterance spoken by a user; identifying a location associated with the speech utterance; determining, by accessing a profile of the user, a probability associated with the user and the location, the probability being an expected type of background noise for the user at the location; and based on the probability, applying a background noise model to recognize the speech utterance. ### Claim 2 (depends on 1) The method of claim 1, wherein the client device is a mobile device. ### Claim 3 (depends on 1) The method of claim 1, wherein determining the probability is based on accessing a stored series of background noises from a background environment associated with the user. ### Claim 4 (depends on 1) The method of claim 1, wherein applying the background noise model comprises compensating a speech recognition model with the background noise model. ### Claim 5 (depends on 1) The method of claim 1, wherein applying the background noise model is performed based on characteristics of the client device. ### Claim 6 (depends on 1) The method of claim 1, further comprising: identifying a time associated with the speech utterance; and determining the probability based on the time. ### Claim 7 A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving a speech utterance from a client device, the speech utterance spoken by a user; identifying a location associated with the speech utterance; determining, by accessing a profile of the user, a probability associated with the user and the location, the probability being an expected type of background noise for the user at the location; and based on the probability, applying a background noise model to recognize the speech utterance. ### Claim 8 (depends on 7) The system of claim 7, wherein the client device is a mobile device. ### Claim 9 (depends on 7) The system of claim 7, wherein determining the probability is based on accessing a stored series of background noises a background environment associated with the user. ### Claim 10 (depends on 7) The system of claim 7, wherein applying the background noise model comprises compensating a speech recognition model with the background noise model. ### Claim 11 (depends on 7) The system of claim 7, wherein applying the background noise model is performed based on characteristics of the client device. ### Claim 12 (depends on 7) The system of claim 7, the computer-readable storage medium having additional instructions stored which result in the operations further comprising: identifying a time associated with the speech utterance; and determining the probability based at least in part on the time. ### Claim 13 A computer-readable storage device having instructions stored which, when executed by a processor, cause the processor to perform operations comprising: receiving a speech utterance from a client device, the speech utterance spoken by a user; identifying a location associated with the speech utterance; determining, by accessing a profile of the user, a probability associated with the user and the location, the probability being an expected type of background noise for the user at the location; and based on the probability, applying a background noise model to recognize the speech utterance. ### Claim 14 (depends on 13) The computer-readable storage device of claim 13, wherein the client device is a mobile device. ### Claim 15 (depends on 13) The computer-readable storage device of claim 13, wherein determining the probability is based on accessing a stored series of background noises from a background environment associated with the user. ### Claim 16 (depends on 13) The computer-readable storage device of claim 13, wherein applying the background noise model comprises compensating a speech recognition model with the background noise model. ### Claim 17 (depends on 13) The computer-readable storage device of claim 13, wherein applying the background noise model is performed based on characteristics of the client device.