Abstract: A system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech of a user that is stored on a user's device or the system, which detects the voice activity, to determine according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech, which is based on the user profile, has a high probability of being a prefix of a longer utterance.

Abstract: A virtual assistant processes natural language expressions according to grammar rules created by domain providers. The virtual assistant uniquely identifies each of a multiplicity of users and stores values of grammar slots filled by natural language expressions from each user. The virtual assistant stores histories of slot values and computes statistics from the history. The virtual assistant provider, or a classification client, provides values of attributes of users as labels for a machine learning classification algorithm. The algorithm processes the grammar slot values and labels to compute probability distributions for unknown attribute values of users. A network effect of users and domain grammars make the virtual assistant useful and provides increasing amounts of data that improve classification accuracy and usefulness.

Abstract: A system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.

Abstract: A system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.

Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.

Abstract: Training and enhancement of neural network models, such as from private data, are described. A slave device receives a version of a neural network model from a master. The slave accesses a local and/or private data source and uses the data to perform optimization of the neural network model. This can be done such as by computing gradients or performing knowledge distillation to locally train an enhanced second version of the model. The slave sends the gradients or enhanced neural network model to a master. The master may use the gradient or second version of the model to improve a master model.

Abstract: Ads are generated based on product info and consumer profiles. A discriminator evaluates probabilities of ads being effective at causing consumer engagement. A decoder extracts product info from generated ads. Based on the probabilities of ads being effective and similarity of extracted and source product info, generated ads are labeled as examples. The examples are used in training an improved ad generator. Ads may be visual and/or audio containing speech. Ads may even contain humor, as recognized by mismatches between source and decoded product info.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: A system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.

Abstract: A processing system detects a period of non-voice activity and compares its duration to a cutoff period. The system adapts the cutoff period based on parsing previously-recognized speech to determine, according to a model, such as a machine-learned model, the probability that the speech recognized so far is a prefix to a longer complete utterance. The cutoff period is longer when a parse of previously recognized speech has a high probability of being a prefix of a longer utterance.

Abstract: To train a speech recognizer, such as for recognizing variables in a neural speech-to-meaning system, compute, within an embedding space, a range of vectors of features of natural speech. Generate parameter sets for speech synthesis and synthesis speech according to the parameters. Analyze the synthesized speech to compute vectors in the embedding space. Using a cost function that favors an even spread (minimal clustering) generates a multiplicity of speech synthesis parameter sets. Using the multiplicity of parameter sets, generate a multiplicity of speech of known words that can be used as training data for speech recognition.

Abstract: A method of providing relevant messages to an automotive virtual assistant is provided. The method includes receiving a spoken utterance and corresponding first geolocation information detected by a subsystem of a first automobile, parsing the spoken utterance to determine concepts and storing the concepts in a concept database indexed by the corresponding first geolocation information. The method further includes receiving second geolocation information detected by a subsystem of a second automobile, searching the concept database for an index based on the second geolocation information to find a stored concept of the stored concepts, searching a natural language expression database using the stored concept as an index to find an assistive natural language expression, wherein the assistive natural language expression includes a constituent part, and sending the assistive natural language expression to the second automobile with the stored concept in place of the constituent part.

Abstract: A method of predicting a person's interests is provided. The method includes receiving geolocation information about a user location, reading, from a database of interpretations, at least one interpretation of an expression made in close proximity to the location, reading, from a database of ad bids, a plurality of ad bids comprising interpretations, comparing the interpretation from the database to the interpretations of the ad bids to select a most valuable ad bid having an interpretation that matches the interpretation of an expression made in close proximity to the location, and presenting an ad associated with the most valuable ad bid, wherein the interpretation is from a natural language expression.

Abstract: Simulation or calculation to estimate activity per unit in a chip design, combined with estimation of the specific location or region in which the unit logic will be finally placed, provides for calculation of an estimation of the activity distribution within the floorplan. Activity distribution estimation can be performed with fine granularity (at a gate level), at coarse granularity (at a macro level), or at an intermediate granularity (at a network-on-chip unit level). The estimation is displayed, visually, to a user of a design tool. Furthermore, the estimation is used to make manual or automatic optimizations of the floorplan and the location and configuration of units within the floorplan.

Abstract: Training and enhancement of neural network models, such as from private data, are described. A slave device receives a version of a neural network model from a master. The slave accesses a local and/or private data source and uses the data to perform optimization of the neural network model. This can be done such as by computing gradients or performing knowledge distillation to locally train an enhanced second version of the model. The slave sends the gradients or enhanced neural network model to a master. The master may use the gradient or second version of the model to improve a master model.

Abstract: Systems and methods for distributed training of a neural network model are described. Various embodiments include a master device and a slave device. The master device has a first version of the neural network model. The slave device is communicatively coupled to a first data source and the master device, and the first data source is inaccessible by the master device, in accordance with one embodiment. The slave device is remote from the master device. The master device is configured to output first configuration data for the neural network model based on the first version of the neural network model. The slave device is configured to use the first configuration data to instantiate a second version of the neural network model. The slave device is configured to train the second version of the neural network model using data from the first data source and to output second configuration data for the neural network model.

Abstract: To train a speech recognizer, such as for recognizing variables in a neural speech-to-meaning system, compute, within an embedding space, a range of vectors of features of natural speech. Generate parameter sets for speech synthesis and synthesis speech according to the parameters. Analyze the synthesized speech to compute vectors in the embedding space. Using a cost function that favors an even spread (minimal clustering) generates a multiplicity of speech synthesis parameter sets. Using the multiplicity of parameter sets, generate a multiplicity of speech of known words that can be used as training data for speech recognition.

Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.