Abstract: Solutions for custom display post processing (DPP) in speech recognition (SR) use a customized multi-stage DPP pipeline that transforms a stream of SR tokens from lexical form to display form. A first transformation stage of the DPP pipeline receives the stream of tokens, in turn, by an upstream filter, a base model stage, and a downstream filter, and transforms a first aspect of the stream of tokens (e.g., disfluency, inverse text normalization (ITN), capitalization, etc.) from lexical form into display form. The upstream filter and/or the downstream filter alter the stream of tokens to change the default behavior of the DPP pipeline into custom behavior. Additional transformation stages of the DPP pipeline perform further transforms, allowing for outputting final text in a display format that is customized for a specific user. This permits each user to efficiently leverage a common baseline DPP pipeline to produce a custom output.

Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.

Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.

Abstract: Systems and methods are disclosed for transitioning communication sessions across services. In one implementation, a first communication is received from a user via a first service. The first communication is associated with a communication session. The first communication is processed to identify a request with respect to a second service. The second service is initiated in response to the request.

Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.

Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.

Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.

Abstract: Generally discussed herein are devices, systems, and methods for wake word verification. A method can include receiving, at a server, a message from a device indicating that an utterance of a user-defined wake word was detected at the device, the message including (a) audio samples or features extracted from the audio samples and (b) data indicating the user-defined wake word, retrieving or generating, at the server, a custom decoding graph for the user-defined wake word, wherein the decoding graph and the static portion of the wake word verification model form a custom wake word verification model for the user-defined wake word, executing the wake word verification model to determine a likelihood that the wake word was uttered, and providing a message to the device indicating whether wake was uttered based on the determined likelihood.

Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.

Abstract: One disclosed example provides a computing system configured to receive input defining a machine conversation dialog flow, display in an editing user interface a first representation of the machine conversation dialog flow in the form of a symbolic representation, receive input requesting display of a second representation of the machine conversation dialog flow, and in response to the request display in the editing user interface the machine conversation dialog flow in the character-based representation. The computing system is further configured to, based upon the machine conversation dialog flow, update a machine conversation schema template to form an updated machine conversation schema, and form an agent definition file based upon the updated machine conversation schema for use in executing the machine conversation dialog flow.

Abstract: Generally discussed herein are devices, systems, and methods for wake word verification. A method can include receiving, at a server, a message from a device indicating that an utterance of a user-defined wake word was detected at the device, the message including (a) audio samples or features extracted from the audio samples and (b) data indicating the user-defined wake word, retrieving or generating, at the server, a custom decoding graph for the user-defined wake word, wherein the decoding graph and the static portion of the wake word verification model form a custom wake word verification model for the user-defined wake word, executing the wake word verification model to determine a likelihood that the wake word was uttered, and providing a message to the device indicating whether wake was uttered based on the determined likelihood.

Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.

Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.

Abstract: A server is provided, including a processor configured to execute a bot server program. The bot server program may receive from a computing device an input with an input type that includes one or more of speech and text. The bot server program may programmatically generate an output, wherein the output is generated based on the input. The bot server program may detect one or more output types capable of being output by the computing device and select an output type from a plurality of output types that may include speech and text. The selected output type may be an output type capable of being output by the computing device. The bot server program may modify the programmatically generated output to produce a modified output with the selected output type, and may convey the modified output to the computing device for output on a display and/or speaker.

Abstract: Computer systems, methods, and storage media for generating a continuous motion control using neurological data and for associating the continuous motion control with a continuous user interface control to enable analog control of the user interface control. The user interface control is modulated through a user's physical movements within a continuous range of motion associated with the continuous motion control. The continuous motion control enables fine-tuned and continuous control of the corresponding user interface control as opposed to control limited to a small number of discrete settings.

Abstract: Computer systems, methods, and storage media for tailoring a user interface to a user according to a determined user state and a determined interface context corresponding to the determined user state. The user interface is tailored by modifying the format of at least a portion of the interface, including modifying the content, layout of the content, presentation sequence, or visual display of the interface. A user interface includes a selectable formatting object for controlling the formatting of the user interface and for generating feedback data for training an ensemble learning component to enable more effective predictive formatting changes.

Abstract: Computer systems, methods, and storage media for generating a continuous motion control using neurological data and for associating the continuous motion control with a continuous user interface control to enable analog control of the user interface control. The user interface control is modulated through a user's physical movements within a continuous range of motion associated with the continuous motion control. The continuous motion control enables fine-tuned and continuous control of the corresponding user interface control as opposed to control limited to a small number of discrete settings.

Abstract: Computer systems, methods, and storage media for generating a continuous motion control using neurological data and for associating the continuous motion control with a continuous user interface control to enable analog control of the user interface control. The user interface control is modulated through a user's physical movements within a continuous range of motion associated with the continuous motion control. The continuous motion control enables fine-tuned and continuous control of the corresponding user interface control as opposed to control limited to a small number of discrete settings.

Abstract: Systems and methods are disclosed for transitioning communication sessions across services. In one implementation, a first communication is received from a user via a first service. The first communication is associated with a communication session. The first communication is processed to identify a request with respect to a second service. The second service is initiated in response to the request.

Abstract: A method for priming an extensible speech recognition system comprises receiving audio language input from a user. The method also comprises receiving an indication that the audio language input is associated with a first language-based intelligent agent. The first language-based intelligent agent is associated with a first grammar set that is specific to the first language-based intelligent agent. Additionally, the method comprises matching one or more spoken words or phrases within the audio language input to text-based words or phrases within a general grammar set associated with a speech recognition system and the first grammar set. The first grammar set is associated with a higher match bias than the general grammar set, such that the speech recognition system is more likely to match the one or more spoken words or phrases to the text-based words or phrases within the first grammar set.