# US 2018/0322876 A1 · Linear scoring for low power wake on voice > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322876 A1 - Document ID: US20180322876A1 - Status: Published application - Kind: A1 - Application number: 16/034,006 - Filed: 2018-07-12 - Published: 2018-11-08 - Earliest claimed: 2016-12-23 - Assignees: Intel Corporation - Applicants: Intel Corporation - Inventors: Tobias BOCKLET; Tomasz DORAU; Przemyslaw SOBON; Przemyslaw TOMASZEWSKI - Web page: https://patentgrep.com/patent/US20180322876A1 ## CPC - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit - G10L 15/08: Speech classification or search - G10L 15/16: using artificial neural networks - G10L 2015/088: {Word spotting} - G10L 2015/223: {Execution procedure of a spoken command} - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Key phrase detection techniques for applications such as wake on voice are discussed include performing a vectorized operation on a multiple element acoustic score vector for a current time instance including a single state rejection model score and scores for a multiple state key phrase model and a multiple element state score vector for a previous time instance including a previous state score for the single state rejection model and previous state scores for the multiple state key phrase model to generate a multiple element score summation vector and a second vectorized operation on the multiple element score summation vector to determine a multiple element state score vector for the current time instance. The multiple element state score vector for the current time instance may then be evaluated to determine whether received audio input includes a key phrase corresponding to the multiple state key phrase model. ## Claims ### Claim at position 1 1-25. (canceled) ### Claim 26 A system to perform key phrase detection comprising: memory to store a state score vector and an acoustic score vector; and a processor coupled to the memory, the processor to: combine the state score vector and the acoustic score vector to generate a score summation vector, wherein the state score vector comprises, for a first time instance, a state score for a single state rejection model and state scores for a multiple state key phrase model corresponding to a predetermined key phrase, and wherein the acoustic score vector comprises, for a second time instance and based on received audio input, an acoustic score for the single state rejection model and acoustic scores for the multiple state key phrase model; determine an updated state score vector for the second time instance based on the multiple element score summation vector, wherein the updated state score vector comprises an updated state score for the single state rejection model and updated state scores for the multiple state key phrase model; evaluate the updated state score for the single state rejection model and a selected updated state score for the multiple state key phrase model to determine whether the received audio input is associated with the predetermined key phrase; and provide a system wake indicator or a command when the received audio input is associated with the predetermined key phrase. ### Claim 27 (depends on 26) The system of claim 26, wherein the score summation vector comprises a rejection state value corresponding to a sum of the acoustic score for the single state rejection model and the state score for the single state rejection model. ### Claim 28 (depends on 27) The system of claim 27, wherein the score summation vector comprises key phrase model values corresponding to sums of the acoustic scores for the multiple state key phrase model and the state scores for the multiple state key phrase model. ### Claim 29 (depends on 28) The system of claim 28, wherein the processor to determine the updated state score vector comprises the processor to determine a maximum of the rejection state value and a first value of the key phrase model values. ### Claim 30 (depends on 29) The system of claim 29, wherein the processor to determine the updated state score vector comprises the processor to determine a maximum of the first value and a second value of the key phrase model values. ### Claim 31 (depends on 30) The system of claim 30, wherein the processor to determine the maximum of the rejection state value and the first value of the key phrase model values and the processor to determine the maximum of the first value and the second value of the key phrase model values are to be performed simultaneously. ### Claim 32 (depends on 29) The system of claim 29, wherein the first value corresponds to an initial state of the multiple state key phrase model. ### Claim 33 (depends on 29) The system of claim 29, wherein the processor to determine the updated state score vector comprises the processor to determine maxima between adjacent remaining values of the key phrase model values to provide the updated state scores for the multiple state key phrase model. ### Claim 34 (depends on 26) The system of claim 26, wherein the processor to combine the state score vector and the acoustic score vector is to add corresponding elements of the state score vector and the acoustic score vector simultaneously. ### Claim 35 (depends on 26) The system of claim 26, the processor to generate the acoustic score vector by being configured to: determine the score for the single state rejection model as a maximum of a best rejection score corresponding to the single state rejection model and a best silence score corresponding to the single state rejection model; and access a deep neural network acoustic model to determine the scores for the multiple state key phrase model. ### Claim 36 (depends on 26) The system of claim 26, wherein the memory and the processor are implemented as a system on a chip. ### Claim 37 A computer-implemented method for key phrase detection comprising: combining a state score vector and an acoustic score vector to generate a score summation vector, wherein the state score vector comprises, for a first time instance, a state score for a single state rejection model and state scores for a multiple state key phrase model corresponding to a predetermined key phrase, and wherein the acoustic score vector comprises, for a second time instance and based on received audio input, an acoustic score for the single state rejection model and acoustic scores for the multiple state key phrase model; determining an updated state score vector for the second time instance based on the multiple element score summation vector, wherein the updated state score vector comprises an updated state score for the single state rejection model and updated state scores for the multiple state key phrase model; evaluating the updated state score for the single state rejection model and a selected updated state score for the multiple state key phrase model to determine whether the received audio input is associated with the predetermined key phrase; and providing a system wake indicator or a command when the received audio input is associated with the predetermined key phrase. ### Claim 38 (depends on 37) The method of claim 37, wherein the score summation vector comprises a rejection state value corresponding to a sum of the acoustic score for the single state rejection model and the state score for the single state rejection model and key phrase model values corresponding to sums of the acoustic scores for the multiple state key phrase model and the state scores for the multiple state key phrase model. ### Claim 39 (depends on 38) The method of claim 38, wherein determining the updated state score vector comprises: determining a maximum of the rejection state value and a first value of the key phrase model values; and determining a maximum of the first value and a second value of the key phrase model values. ### Claim 40 (depends on 38) The method of claim 38, wherein determining the updated state score vector comprises: determining maxima between adjacent remaining values of the key phrase model values to provide the updated state scores for the multiple state key phrase model. ### Claim 41 At least one non-transitory computer readable medium comprising a plurality of instruction that, in response to being executed on a device, cause the device to perform key phrase detection by: combining a state score vector and an acoustic score vector to generate a score summation vector, wherein the state score vector comprises, for a first time instance, a state score for a single state rejection model and state scores for a multiple state key phrase model corresponding to a predetermined key phrase, and wherein the acoustic score vector comprises, for a second time instance and based on received audio input, an acoustic score for the single state rejection model and acoustic scores for the multiple state key phrase model; determining an updated state score vector for the second time instance based on the multiple element score summation vector, wherein the updated state score vector comprises an updated state score for the single state rejection model and updated state scores for the multiple state key phrase model; evaluating the updated state score for the single state rejection model and a selected updated state score for the multiple state key phrase model to determine whether the received audio input is associated with the predetermined key phrase; and providing a system wake indicator or a command when the received audio input is associated with the predetermined key phrase. ### Claim 42 (depends on 41) The non-transitory computer readable medium of claim 41, wherein the score summation vector comprises a rejection state value corresponding to a sum of the acoustic score for the single state rejection model and the state score for the single state rejection model and key phrase model values corresponding to sums of the acoustic scores for the multiple state key phrase model and the state scores for the multiple state key phrase model. ### Claim 43 (depends on 42) The non-transitory computer readable medium of claim 42, wherein determining the updated state score vector comprises: determining a maximum of the rejection state value and a first value of the key phrase model values; and determining a maximum of the first value and a second value of the key phrase model values. ### Claim 44 (depends on 42) The non-transitory computer readable medium of claim 42, wherein determining the updated state score vector comprises: determining maxima between adjacent remaining values of the key phrase model values to provide the updated state scores for the multiple state key phrase model. ### Claim 45 (depends on 41) The non-transitory computer readable medium of claim 41, wherein generating the acoustic score vector comprises: determining the score for the single state rejection model as a maximum of a best rejection score corresponding to the single state rejection model and a best silence score corresponding to the single state rejection model; and accessing a deep neural network acoustic model to determine the scores for the multiple state key phrase model.