Knowles Electronics, Inc.

Sridhar Krishna Nemala

David Klein

Jean Laroche

Carlo Murgia

Michael M Goodwin

John Woodruff

Ye Jiang

Tony S Verma

Eitan Medina

Peter Santos

Umit Yapanel

Deborah Kathleen Vitus

Frederic Caldwell

Hong You

Systems and methods for performing personalized operations of a mobile device are provided. An example method includes determining a sensor signature has been received. The sensor signature may be a combination of acoustic and non-acoustic inputs. A classification or recognition or matching score for the received sensor signature may be calculated. A context associated with one or more of the operations of the mobile device may be determined. A confidence level or weight may be determined for the acoustic and non-acoustic inputs based on the context. Based on the confidence level or weight, a determination may be made whether to perform the one or more operations. The determination whether to perform the one or more operations may be further based on a combination of the confidence level or weight and the score. Determining the context can include identifying the type and level of noise associated with the mobile device's environment.

Personalized operation of a mobile device using sensor signatures

Systems and methods for voice authentication are provided. An example method starts with dynamically generating authentication information and a prompt associated therefor. The authentication information is generated to emphasize differences between an authorized user and others. The authentication information may be generated based on at least one of a user profile, an acoustic environment in which a mobile device is located, and a history of interactions with the mobile device. The authentication information may be an answer to a question which the authorized user would uniquely be able to provide, or it may be a distinctive password. The prompt, for the authentication information, is provided to a user attempting to use the mobile device. The method proceeds with capturing an acoustic sound of a speech of the user and detecting the authentication information in the speech. Based on the detection, a confidence score is determined. Based on the confidence score and context, the method performs an authentication of the user or providing the confidence score, the context, and a transcription of the speech to a next stage of authentication.

Shared secret voice authentication

Systems and methods for multi-sourced noise suppression are provided. An example system may receive streams of audio data including a voice signal and noise, the voice signal including a spoken word. The streams of audio data are provided by distributed audio devices. The system can assign weights to the audio streams based at least partially on quality of the audio streams. The weights of audio streams can be determined based on signal-to-noise ratios (SNRs). The system may further process, based on the weights, the audio stream to generate cleaned speech. Each audio device comprises microphone(s) and can be associated with the Internet of Things (IoT), such that the audio devices are Internet of Things devices. The processing can include noise suppression and reduction and echo cancellation. The cleaned speech can be provided to a remote device for further processing which may include Automatic Speech Recognition (ASR).

Multi-sourced noise suppression

Described are noise suppression techniques applicable to various systems including automatic speech processing systems in digital audio pre-processing. The noise suppression techniques utilize a machine-learning framework trained on cues pertaining to reference clean and noisy speech signals, and a corresponding synthetic noisy speech signal combining the clean and noisy speech signals. The machine-learning technique is further used to process audio signals in real time by extracting and analyzing cues pertaining to noisy speech to dynamically generate an appropriate gain mask, which may eliminate the noise components from the input audio signal. The audio signal pre-processed in such a manner may be applied to an automatic speech processing engine for corresponding interpretation or processing. The machine-learning technique may enable extraction of cues associated with clean automatic speech processing features, which may be used by the automatic speech processing engine for various automatic speech processing.

Noise suppression for speech processing based on machine-learning mask estimation

Methods for voice sensing and keyword analysis are provided. An example method allows for causing a mobile device to transition to a second power mode, from a first power mode, in response to a first acoustic signal. The method includes authenticating a user based at least in part on a second acoustic signal. While authenticating the user, the second acoustic signal is compared to a spoken keyword. The spoken keyword is analyzed for authentication strength based on the length of the spoken keyword, quality of a series of phonemes used to represent the spoken keyword, and likelihood of the series of phonemes to be detected by a voice sensing. While receiving the first and second acoustic signals, a signal to noise ratio (SNR) is determined. The SNR is used to adjust sensitivity of a detection threshold of a voice sensing.

Voice sensing and keyword analysis

Growing community of inventors

Mountain View, CA, United States of America

Sridhar Krishna Nemala