# US 2014/0207447 A1 · Voice identification method and apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0207447 A1 - Document ID: US20140207447A1 - Status: Published application - Kind: A1 - Application number: 14/100,884 - Filed: 2013-12-09 - Published: 2014-07-24 - Earliest claimed: 2013-01-24 - Assignees: Huawei Device Co., Ltd. - Applicants: Huawei Device Co., Ltd. - Inventors: Hongrui JIANG; Xiyong WANG; Junbin LIANG; Weijun ZHENG; Junyang ZHOU - Web page: https://patentgrep.com/patent/US20140207447A1 ## CPC - G10L 15/20: Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech (G10L21/02 takes precedence) - G10L 15/08: Speech classification or search - G10L 25/24: the extracted parameters being the cepstrum - G10L 25/84: for discriminating voice from noise - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Embodiments of the present invention provide a voice identification method, which includes: obtaining voice data; obtaining a confidence value according to the voice data; obtaining a noise scenario according to the voice data; obtaining a confidence threshold corresponding to the noise scenario; and if the confidence value is greater than or equal to the confidence threshold, processing the voice data. An apparatus is also provided. The method and apparatus that flexibly adjust the confidence threshold according to the noise scenario greatly improve a voice identification rate under a noise environment. ## Claims ### Claim 1 A voice identification method, comprising: obtaining voice data; obtaining a confidence value according to the voice data; obtaining a noise scenario according to the voice data; obtaining a confidence threshold corresponding to the noise scenario; and processing the voice data, if the confidence value is greater than or equal to the confidence threshold. ### Claim 2 (depends on 1) The method according to claim 1, wherein the noise scenario comprises: a noise type; and a noise magnitude. ### Claim 3 (depends on 2) The method according to claim 2, wherein the obtaining the noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a frequency cepstrum coefficient of a noise in the voice data; and obtaining, according to the frequency cepstrum coefficient of the noise and a pre-established noise type model, the noise type of the voice data. ### Claim 4 (depends on 3) The method according to claim 3, wherein the noise type model is established by implementing the following: obtaining noise data; obtaining a frequency cepstrum coefficient of the noise data according to the noise data; and processing the frequency cepstrum coefficient according to an Expectation-maximization (EM) algorithm, and establishing a noise type model. ### Claim 5 (depends on 3) The method according to claim 3, wherein the noise type model is a Gaussian mixture model. ### Claim 6 (depends on 2) The method according to claim 2, wherein the obtaining the noise scenario according to the voice data specifically comprises: obtaining, according to the voice data, a feature parameter of the voice data; performing voice activity detection according to the feature parameter; and obtaining the noise magnitude according to a result of the voice activity detection. ### Claim 7 (depends on 6) The method according to claim 6, wherein the noise magnitude specifically comprises: a signal-to-noise ratio; and a noise energy level. ### Claim 8 (depends on 1) The method according to claim 1, wherein the obtaining the confidence threshold corresponding to the noise scenario comprises: obtaining, according to a correspondence between pre-stored empirical data of a confidence threshold and the noise scenario, the confidence threshold corresponding to the noise scenario. ### Claim 9 (depends on 1) The method according to claim 1, further comprising: prompting a user if the confidence value is smaller than the confidence threshold. ### Claim 10 A voice identification apparatus, comprising: an obtaining unit, configured to obtain voice data; a confidence value unit, configured to receive the voice data obtained by the obtaining unit, and obtain a confidence value according to the voice data; a noise scenario unit, configured to receive the voice data obtained by the obtaining unit, and obtain a noise scenario according to the voice data; a confidence threshold unit, configured to receive the noise scenario of the noise scenario unit, and obtain a confidence threshold corresponding to the noise scenario; and a processing unit, configured to receive the confidence value obtained by the confidence value unit and the confidence threshold obtained by the confidence threshold unit, and if the confidence value is greater than or equal to the confidence threshold, process the voice data. ### Claim 11 (depends on 10) The apparatus according to claim 10, further comprising: a model establishing unit, configured to obtain noise data, obtain a frequency cepstrum coefficient of the noise data according to the noise data, process the frequency cepstrum coefficient according to an Expectation-maximization (EM) algorithm, and establish a noise type model. ### Claim 12 (depends on 11) The apparatus according to claim 11, wherein the noise scenario unit specifically comprises: a noise type unit, configured to obtain, according to the voice data of the obtaining unit, a frequency cepstrum coefficient of a noise in the voice data, and obtain, according to the frequency cepstrum coefficient of the noise and the noise type model of the model establishing unit, a noise type of the voice data. ### Claim 13 (depends on 10) The apparatus according to claim 10, wherein the noise scenario unit further comprises: a noise magnitude unit, configured to obtain, according to the voice data of the obtaining unit, a feature parameter of the voice data, perform voice activity detection according to the feature parameter, and obtain a noise magnitude according to a result of the voice activity detection. ### Claim 14 (depends on 10) The apparatus according to claim 10, further comprising: a storage unit, configured to store empirical data of a confidence threshold. ### Claim 15 (depends on 14) The apparatus according to claim 14, wherein the confidence threshold unit is configured to obtain, according to a correspondence between the stored empirical data of the confidence threshold pre-stored by the storage unit and the noise scenario, the confidence threshold corresponding to the noise scenario. ### Claim 16 A mobile terminal, comprising a processor and a microphone, wherein the microphone is configured to obtain voice data; and the processor is configured to obtain a confidence value and a noise scenario according to the voice data, obtain, according to the noise scenario, a confidence threshold corresponding to the noise scenario, and if the confidence value is greater than or equal to the confidence threshold, process the voice data. ### Claim 17 (depends on 16) The mobile terminal according to claim 16, further comprising a memory, configured to store empirical data of a confidence threshold. ### Claim 18 (depends on 17) The mobile terminal according to claim 17, wherein the processor is configured to obtain the confidence value and the noise scenario according to the voice data; obtain, according to a correspondence between the stored empirical data of the confidence threshold pre-stored by the memory and the noise scenario, the confidence threshold corresponding to the noise scenario; and process the voice data if the confidence value is greater than or equal to the confidence threshold.