# US 12,603,088 B2 · Training a device specific acoustic model > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,603,088 B2 - Document ID: US12603088B2 - Status: Granted - Kind: B2 - Application number: 18/379,618 - Filed: 2023-10-12 - Published: 2024-02-01 - Granted: 2026-04-14 - Earliest claimed: 2018-06-01 - Assignees: SoundHound AI IP, LLC - Applicants: SoundHound AI IP, LLC - Inventors: Keyvan Mohajer; Mehul Patel - Web page: https://patentgrep.com/patent/US12603088B2 ## CPC - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G06F 3/167: {Audio in a user interface, e.g. using voice commands for navigating, audio feedback} - G10L 15/18: using natural language modelling - G10L 15/183: using context dependencies, e.g. language models - G10L 15/187: Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Custom acoustic models can be configured by developers by providing audio files with custom recordings. The custom acoustic model is trained by tuning a baseline model using the audio files. Audio files may contain custom noise to apply to clean speech for training. The custom acoustic model is provided as an alternative to a standard acoustic model. A speech recognition system can select an acoustic model for use upon receiving metadata about the device conditions or type. Speech recognition is performed on speech audio using one or more acoustic models. The result can be provided to developers through the user interface, and an error rate can be computed and also provided. ## Claims ### Claim 1 A method of performing speech recognition comprising: storing a plurality of acoustic models associated with a device; receiving metadata including information according to which one or more trained acoustic models, of the plurality of acoustic models that are already trained, is selected; preselecting, based on the metadata, one or more trained acoustic models of the plurality of acoustic models; receiving speech audio including natural language utterances; selecting a trained acoustic model from the preselected one or more trained acoustic models, the selected trained acoustic model being trained with environmental features; and employing the selected trained acoustic model to recognize speech from the natural language utterances included in the received speech audio. ### Claim 2 (depends on 1) The method of claim 1, wherein the plurality of acoustic models are associated with different device conditions, and the metadata is indicative of a device condition. ### Claim 3 (depends on 2) The method of claim 2, wherein the device conditions include usage conditions of the device. ### Claim 4 (depends on 3) The method of claim 3, wherein usage conditions of the device provide information regarding one or more hardware and software components of the device for receiving the speech audio or for providing audio feedback to a user of the device. ### Claim 5 (depends on 1) The method of claim 1, wherein the received metadata is stored within the device. ### Claim 6 (depends on 1) The method of claim 1, wherein the plurality of acoustic models are associated with different device types, and the metadata is indicative of a device type. ### Claim 7 (depends on 6) The method of claim 6, wherein the device type identifies at least one of a model number and serial number of the device. ### Claim 8 (depends on 1) The method of claim 1, wherein employing the selected trained acoustic model to recognize speech comprises extraction of phonemes from the received speech audio. ### Claim 9 (depends on 1) The method of claim 1, wherein the device is an indoor appliance. ### Claim 10 (depends on 1) The method of claim 1, wherein the device is a mobile device. ### Claim 11 (depends on 1) The method of claim 1, wherein the plurality of acoustic models are stored, at least in part, in the cloud. ### Claim 12 A non-transitory computer readable medium storing code that, if executed by one or more computers, would cause the one or more computers to: store a plurality of acoustic models associated with a device; receive metadata including information according to which one or more trained acoustic models, of the plurality of acoustic models that are already trained, is selected; preselect, based on the metadata, one or more trained acoustic models of the plurality of acoustic models; receive speech audio including natural language utterances; select a trained acoustic model from the preselected one or more trained acoustic models, the selected trained acoustic model being trained with environmental features; and employ the selected trained acoustic model to recognize speech from the natural language utterances included in the received speech audio. ### Claim 13 A method of using a platform for configuring device-specific speech recognition, the method comprising: receiving a selection of a set of at least two acoustic models appropriate for a specific type of a device, the selection of the set of at least two acoustic models being received by an interaction with a graphical user interface provided by a computer system; and providing received speech audio and metadata to a speech recognition system associated with the platform. ### Claim 14 (depends on 13) The method of claim 13 further comprising: providing a custom acoustic model appropriate for the specific type of the device, wherein the set of selected acoustic models includes the provided custom acoustic model. ### Claim 15 (depends on 13) The method of claim 13 further comprising: providing training data for training an acoustic model appropriate to the specific type of the device; and selecting an acoustic model trained on the provided training data. ### Claim 16 (depends on 13) The method of claim 13, wherein the metadata identifies an acoustic model of the set according to the specific type of the device. ### Claim 17 (depends on 13) The method of claim 13, wherein the metadata identifies a specific device condition, and a computer system selects an acoustic model from the set of acoustic models in dependence upon the specific device condition. ### Claim 18 (depends on 13) The method of claim 13, wherein the at least two acoustic models recognize speech by extraction of phonemes from the received speech audio. ### Claim 19 (depends on 13) The method of claim 13, wherein the interaction is a physical interaction. ### Claim 20 A non-transitory computer readable medium storing code that, if executed by one or more computers, would cause the one or more computers to: detect information useful for selecting an acoustic model from a plurality of acoustic models that are already trained and indicative of a device condition; receive speech audio; transmit the detected information and the received speech audio; and receive information requested by speech in the speech audio, wherein the detected information is capable of being employed to preselect and select a trained acoustic model from a plurality of acoustic models associated with different device conditions, the trained acoustic model being trained with environmental features, and the detected information being capable of being used to recognize speech from the received speech audio.