Abstract: A method of presenting a signal to a speech processing engine is disclosed. According to an example of the method, an audio signal is received via a microphone. A portion of the audio signal is identified, and a probability is determined that the portion comprises speech directed by a user of the speech processing engine as input to the speech processing engine. In accordance with a determination that the probability exceeds a threshold, the portion of the audio signal is presented as input to the speech processing engine. In accordance with a determination that the probability does not exceed the threshold, the portion of the audio signal is not presented as input to the speech processing engine.

Abstract: Human-machine interfaces may capture interactions by humans with robots (e.g., robots with a humanoid appearance), the interactions taking a variety of forms (e.g., audio, visual), and may determine an intent of the humans or meaning of human responses via analysis of the interactions. Intent can be determined based on analysis of aural response, including meaning or semantics and/or tone. Intent can be determined based on analysis of visually detectable responses, including head motions, facial gestures, hand or arm gestures, eye gestures. Responses may be compared for consistency. Humans may be queried to confirm determined intended response.

Abstract: Embodiments of the present invention determine a curiosity of a user based on data received from an electronic device associated with the user, where the data includes audible speech captured from user and one or more facial expressions of the user. Embodiments of the present invention identify a first wavelength for audible speech from the user to initiate a command detection mode based on a plurality of wavelengths associated with a user profile for the user. Embodiments of the present invention identify a topic for the audible speech from the user and responsive to determining an intelligent virtual assistant is an intended recipient based on the topic, suspend an activation word for the intelligent virtual assistant.

Abstract: Disclosed is an electronic apparatus. The electronic apparatus includes a communicator comprising communication circuitry, and a processor configured to control the electronic apparatus to, in response to a call request being received through the communicator, transmit CAPTCHA information to an external device that requests the call, and in response to receiving response information about the CAPTCHA information from the external device, identify a counterpart that requests the call based on whether the response information is matched with the CAPTCHA information, and provide information on the identified counterpart.

Abstract: A brain-inspired spoken language understanding system, comprises: a first module that facilitates the conversion of a voice input into phoneme sequences; a buffer module that facilitates the storing of the phoneme sequences until they are clustered into a first storage as one or more meaningful thought representations; a second module that facilitates the monitoring of the reasoning, disambiguation, and prioritization of the system, in addition to controlling the system; a seventh module and an eighth module that facilitate the capturing of at least one non-verbal signal from a conversation, and create at least one time-synchronized non-verbal object, with the help of a third knowledge base; a third module that facilitates the conversion of the phoneme sequences and at least one time-synchronized non-verbal object into sequences of phonetics-based words; a fourth module that facilitates the conversion of the phonetics-based words into sequences of thought representations, with the help of a first knowledge ba

Abstract: Recommending an automated assistant action for inclusion in an existing automated assistant routine of a user, where the existing automated assistant routine includes a plurality of preexisting automated assistant actions. If the user confirms the recommendation through affirmative user interface input, the automated assistant action can be automatically added to the existing automated assistant routine. Thereafter, when the automated assistant routine is initialized, the preexisting automated assistant actions of the routine will be performed, as well as the automated assistant action that was automatically added to the routine in response to affirmative user interface input received in response to the recommendation.

Abstract: Implementations set forth herein relate to phasing-out of vehicle computing device versions while ensuring useful responsiveness of any vehicle computing device versions that are still in operation. Certain features of updated computing devices may not be available to prior versions of computing devices because of hardware limitations. The implementations set forth herein eliminate crashes and wasteful data transmissions caused by prior versions of computing devices that have not been, or cannot be, upgraded. A server device can be responsive to a particular intent request provided to a vehicle computing device, despite the intent request being associated with an action that a particular version of the vehicle computing device cannot execute. In response, the server device can elect to provide speech to text data, and/or natural language understanding data, in furtherance of allowing the vehicle computing device to continue leveraging resources at the server device.

Abstract: A system for automatically labeling data using conceptual descriptions. In one example, the system includes an electronic processor configured to generate unlabeled training data examples from one or more natural language documents and, for each of a plurality of categories, determine one or more concepts associated with a conceptual description of the category and generate a weak annotator for each of the one or more concepts. The electronic processor is also configured to apply each weak annotator to each training data example and, when a training data example satisfies a weak annotator, output a category associated with the weak annotator. For each training data example, the electronic processor determines a probabilistic distribution of the plurality of categories. For each training data example, the electronic processor labels the training data example with a category having the highest value in the probabilistic distribution determined for the training data example.

Abstract: Generating and/or recommending command bundles for a user of an automated assistant. A command bundle comprises a plurality of discrete actions that can be performed by an automated assistant. One or more of the actions of a command bundle can cause transmission of a corresponding command and/or other data to one or more devices and/or agents that are distinct from devices and/or agents to which data is transmitted based on other action(s) of the bundle. Implementations determine command bundles that are likely relevant to a user, and present those command bundles as suggestions to the user. In some of those implementations, a machine learning model is utilized to generate a user action embedding for the user, and a command bundle embedding for each of a plurality of command bundles. Command bundle(s) can be selected for suggestion based on comparison of the user action embedding and the command bundle embeddings.

Abstract: Systems and methods are described herein for locally interpreting a voice query and for managing a storage size of data stored locally to support such local interpretation of voice queries. A voice query is received and compared with a plurality of stored voice queries having similar audio characteristics. If a match is identified, text corresponding to the matching stored voice query is retrieved, and an action corresponding to the retrieved text is performed. If the locally stored table does not contain a stored voice query that matches the voice query, the voice query is transmitted to a remote server for transcription. Once the transcription is received from the remote server, the voice query and the transcription are stored in the table in association with one another.

Abstract: An apparatus comprises processing circuitry configured to pre-process text data for inputting to a trained model, the pre-processing comprising: receiving a set of text data including numerical information, the set of text data comprising a plurality of tokens, wherein a first subset of the plurality of tokens comprises tokens that do not comprise numerical information, and a second subset of the plurality of tokens comprises tokens that each comprise respective numerical information; transforming each of the plurality of tokens into a respective encoding vector, each of the plurality of tokens in the second subset having a common encoding vector; assigning a respective numerical vector to each of the plurality of tokens, wherein each token in the second subset is assigned a respective numerical vector in dependence on the numerical information in said token; and combining the encoding vectors and numerical vectors to obtain a vector representation of the text data.

Abstract: A method, performed by an electronic device, of providing a conversational service includes: receiving an utterance input; identifying a temporal expression representing a time in a text obtained from the utterance input; determining a time point related to the utterance input based on the temporal expression; selecting a database corresponding to the determined time point from among a plurality of databases storing information about a conversation history of a user using the conversational service; interpreting the text based on information about the conversation history of the user, the conversation history information being acquired from the selected database; generating a response message to the utterance input based on a result of the interpreting; and outputting the generated response message.

Abstract: Generating and/or recommending command bundles for a user of an automated assistant. A command bundle comprises a plurality of discrete actions that can be performed by an automated assistant. One or more of the actions of a command bundle can cause transmission of a corresponding command and/or other data to one or more devices and/or agents that are distinct from devices and/or agents to which data is transmitted based on other action(s) of the bundle. Implementations determine command bundles that are likely relevant to a user, and present those command bundles as suggestions to the user. In some of those implementations, a machine learning model is utilized to generate a user action embedding for the user, and a command bundle embedding for each of a plurality of command bundles. Command bundle(s) can be selected for suggestion based on comparison of the user action embedding and the command bundle embeddings.

Abstract: Embodiments described herein provide systems and methods for an Explicit Memory Tracker (EMT) that tracks each rule sentence to perform decision making and to generate follow-up clarifying questions. Specifically, the EMT first segments the regulation text into several rule sentences and allocates the segmented rule sentences into memory modules, and then feeds information regarding the user scenario and dialogue history into the EMT sequentially to update each memory module separately. At each dialogue turn, the EMT makes a decision among based on current memory status of the memory modules whether further clarification is needed to come up with an answer to a user question. The EMT determines that further clarification is needed by identifying an underspecified rule sentence span by modulating token-level span distributions with sentence-level selection scores. The EMT extracts the underspecified rule sentence span and rephrases the underspecified rule sentence span to generate a follow-up question.

Abstract: A method of presenting a signal to a speech processing engine is disclosed. According to an example of the method, an audio signal is received via a microphone. A portion of the audio signal is identified, and a probability is determined that the portion comprises speech directed by a user of the speech processing engine as input to the speech processing engine. In accordance with a determination that the probability exceeds a threshold, the portion of the audio signal is presented as input to the speech processing engine. In accordance with a determination that the probability does not exceed the threshold, the portion of the audio signal is not presented as input to the speech processing engine.

Abstract: A computing system for generating image data representing a speaker's face includes a detection device configured to route data representing a voice signal to one or more processors and a data processing device comprising the one or more processors configured to generate a representation of a speaker that generated the voice signal in response to receiving the voice signal. The data processing device executes a voice embedding function to generate a feature vector from the voice signal representing one or more signal features of the voice signal, maps a signal feature of the feature vector to a visual feature of the speaker by a modality transfer function specifying a relationship between the visual feature of the speaker and the signal feature of the feature vector; and generates a visual representation of at least a portion of the speaker based on the mapping, the visual representation comprising the visual feature.

Abstract: A method, system and computer program product for speech recognition using multiple languages includes receiving, by one or more processors, an input from a user, the input includes a sentence in a first language. The one or more processors translate the sentence to a plurality of languages different than the first language, and create vectors associated with the plurality of languages, each vector includes a representation of the sentence in each of the plurality of languages. The one or more processors calculate eigenvectors for each vector associated with a language in the plurality of languages, and based on the calculated eigenvectors, a score is assigned to each of the plurality of languages according to a relevance for determining a meaning of the sentence.

Abstract: A loss function of a signal including an audio signal is determined. A loss function determining system for an audio signal is provided. A loss function is determined by: determining a reference quantization index by quantizing an original input signal; inputting the original input signal to a neural network classifier and applying an activation function to an output layer of the neural network classifier; and determining a total loss function for the neural network classifier using an output of the activation function and the reference quantization index.

Abstract: An electronic device and method are disclosed herein. The electronic device includes a microphone, a camera, an output device, a memory, and a processor. The processor implements the method, including receiving a voice input and/or capturing an image, and analyze the first voice input or the image to determine at least one of a user's intent, emotion, and situation based on predefined keywords and expressions, identifying a category based on the input, selecting first information based on the category, selecting and outputting a first query prompting confirmation of output of the first information, detect a first responsive input to the first query, and when a condition to output the first information is satisfied, output a second query, detecting a second input responsive to the second query, and selectively outputting the first information based on the second input.

Abstract: Machine learned models take in vectors representing desired behaviors and generate voice vectors that provide the parameters for text-to-speech (TTS) synthesis. Models may be trained on behavior vectors that include user profile attributes, situational attributes, or semantic attributes. Situational attributes may include age of people present, music that is playing, location, noise, and mood. Semantic attributes may include presence of proper nouns, number of modifiers, emotional charge, and domain of discourse. TTS voice parameters may apply per utterance and per word as to enable contrastive emphasis.