# US 11,308,946 B2 · Methods and apparatus for ASR with embedded noise reduction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,946 B2 - Document ID: US11308946B2 - Status: Granted - Kind: B2 - Application number: 16/521,641 - Filed: 2019-07-25 - Published: 2019-11-14 - Granted: 2022-04-19 - Earliest claimed: 2017-01-26 - Assignees: Cerence Operating Company - Applicants: Cerence Operating Company - Inventors: Jianzhong Teng; Xiao-Lin Ren; Xingui Zeng; Yi Gao - Web page: https://patentgrep.com/patent/US11308946B2 ## 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) - G06F 3/165: {Management of the audio stream, e.g. setting of volume, audio stream path} - G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit - G10L 15/08: Speech classification or search - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G10L 21/0232: Processing in the frequency domain - G10L 25/24: the extracted parameters being the cepstrum - G10L 25/84: for discriminating voice from noise - G10L 2015/088: {Word spotting} - 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 Methods and an apparatus for performing feature extraction on speech in a microphone signal with embedded noise processing to reduce the amount of processing are provided. In embodiments, feature extraction and the noise estimate use an output of the same Fourier Transform, such that the noise filtering of the speech is embedded with the feature extraction of the speech. ## Claims ### Claim 1 A method, comprising: receiving a microphone signal; determining whether a noise level of the microphone signal exceeds a noise threshold; selecting between a first signal processing procedure and a second signal processing procedure, different from the first signal processing procedure, based on whether the noise level exceeds the threshold; performing the first signal processing procedure when the noise level of the microphone signal exceeds the noise threshold, the first signal processing procedure including: determining whether the microphone signal contains speech; determining a noise estimate for the microphone signal when speech is found not to be present; performing noise filtering using the noise estimate on the microphone signal when speech is found to be present; and performing feature extraction on the microphone signal when speech is found to be present, such that the noise filtering of the speech is embedded with the feature extraction of the speech; and performing the second signal processing procedure when the noise level of the microphone signal does not exceed the noise threshold, the second signal processing procedure including performing feature extraction on the microphone signal. ### Claim 2 (depends on 1) The method according to claim 1, wherein the noise estimate and the noise filtering are not performed on a same frame of the microphone signal. ### Claim 3 (depends on 1) The method according to claim 1, further including determining whether the microphone signal contains a wake-up phrase after the noise filtering. ### Claim 4 (depends on 1) The method according to claim 1, wherein the feature extraction is performed while a device containing the microphone is in a sleep state. ### Claim 5 (depends on 1) The method according to claim 1, wherein the feature extraction includes the use of mel-frequency cepstral coefficients (MFCCs). ### Claim 6 (depends on 1) The method according to claim 1, further including using a main processor and a lower power processor to provide processing of a wakeup phrase for a device. ### Claim 7 (depends on 6) The method according to claim 6, wherein the lower power processor performs the feature extraction and noise processing to identify the wakeup phrase. ### Claim 8 A system comprising: a processor and memory configured to: determine whether a noise level of a microphone signal exceeds a noise threshold; select between a first signal processing procedure and a second signal processing procedure, different from the first signal processing procedure, based on whether the noise level exceeds the threshold; perform the first signal processing procedure when the noise level of the microphone signal exceeds the noise threshold, the first signal processing procedure including: determining whether the microphone signal contains speech; determining a noise estimate for the microphone signal when speech is found not to be present; performing noise filtering using the noise estimate on the microphone signal when speech is found to be present; and performing feature extraction on the microphone signal when speech is found to be present, such that the noise filtering of the speech is embedded with the feature extraction of the speech; and perform the second signal processing procedure when the noise level of the microphone signal does not exceed the noise threshold, the second signal processing procedure including performing feature extraction on the microphone signal. ### Claim 9 (depends on 8) The system according to claim 8, wherein the noise estimate and the noise filtering are not performed on a same frame of the microphone signal. ### Claim 10 (depends on 8) The system according to claim 8, further including determining whether the microphone signal contains a wake-up phrase after the noise filtering. ### Claim 11 (depends on 8) The system according to claim 8, wherein the feature extraction is performed while a device containing the microphone is in a sleep state. ### Claim 12 (depends on 8) The system according to claim 8, wherein the feature extraction includes the use of mel-frequency cepstral coefficients (MFCCs). ### Claim 13 (depends on 8) The system according to claim 8, further including using a main processor and a lower power processor to provide processing of a wakeup phrase for a device. ### Claim 14 (depends on 13) The system according to claim 13, wherein the lower power processor performs the feature extraction and noise processing to identify the wakeup phrase. ### Claim 15 An article, comprising: a non-transitory computer readable medium having stored instructions that enable a machine to: determine whether a microphone signal has a noise level above a noise threshold; select between a first signal processing procedure and a second signal processing procedure, different from the first signal processing procedure, based on whether the noise level is above the threshold; perform the first signal processing procedure when the noise level of the microphone signal is above the noise threshold, the first signal processing procedure including: determining whether the microphone signal contains speech; determining a noise estimate for the microphone signal when speech is found not to be present; perform noise filtering using the noise estimate on the microphone signal when speech is found to be present; and performing feature extraction on the microphone signal when speech is found to be present, such that the noise filtering of the speech is embedded with the feature extraction of the speech; and perform a second signal processing procedure when the noise level of the microphone signal is not above the noise threshold, the second signal processing procedure including performing feature extraction on the microphone signal. ### Claim 16 (depends on 15) The article according to claim 15, wherein the noise estimate and the noise filtering are not performed on a same frame of the microphone signal. ### Claim 17 (depends on 15) The article according to claim 15, further including instructions for determining whether the microphone signal contains a wake-up phrase after the noise filtering. ### Claim 18 (depends on 15) The article according to claim 15, wherein the feature extraction is performed while a device containing the microphone is in a sleep state. ### Claim 19 (depends on 15) The article according to claim 15, wherein the feature extraction includes the use of mel-frequency cepstral coefficients (MFCCs). ### Claim 20 (depends on 15) The article according to claim 15, further including using a main processor and a lower power processor to provide processing of a wakeup phrase for a device. ### Claim 21 (depends on 6) The method of claim 6, wherein the lower power processor performs denoising and feature extraction for an automatic speech recognition operation performed by the main processor.