Abstract: 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.

Abstract: Systems and methods for performing personalized operations of a mobile device are provided. An example method includes determining a user-defined signature has been received. The user-defined signature can include a combination of an acoustic input and non-acoustic input. The method can further include performing, in response to the determination, operations of the mobile device, the operations being associated with the user-defined signature. Exemplary acoustic input includes a voice sound with at least one spoken keyword. The spoken keyword is used to identify the operations. Exemplary non-acoustic input includes motions of the mobile device detected by an accelerometer, a gyroscope, and a magnetometer. Exemplary motions include vibrations of the mobile device, tapping the mobile device, rotations of the mobile device, movements of the mobile device as to draw a figure in a space.

Abstract: Systems and methods for assisting automatic speech recognition (ASR) are provided. An example system includes a buffer operable to store sensor data. The sensor data includes an acoustic signal, the acoustic signal representing at least one captured sound. The system includes a processor communicatively coupled to the buffer and being operable to store received sensor data in the buffer. The received sensor data is analyzed to produce new parameters associated with the sensor data. The buffered sensor data is processed based at least on the new parameters. The processing may include separating clean voice from noise in the acoustic signal. The processor is further operable to provide at least the processed sensor data (for example, the clean voice) to an ASR system operable to receive and process the processed sensor data at a speed faster than real time. The new parameters may also be provided to the ASR system.

Abstract: 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.

Abstract: Systems and methods for vocal keyword training from text are provided. In one example method, text is received via keyboard or a touch screen. The text can include one or more words of language known to a user. The received text can be compiled to generate a signature. The signature can embody a spoken keyword and include a sequence of phonemes or a triphone. The signature can be provided as an input to automatic speech recognition (ASR) software for subsequent comparison to an audible input. In various embodiments, a mobile device receives the audible input and the text, and at least one of the compiling and ASR functionality is distributed to a cloud-based system.