Abstract: Techniques are described herein for enabling the use of “dynamic” or “context-specific” hot words to invoke an automated assistant. In various implementations, an automated assistant may be executed in a default listening state at least in part on a user's computing device(s). While in the default listening state, audio data captured by microphone(s) may be monitored for default hot words. Detection of the default hot word(s) transitions of the automated assistant into a speech recognition state. Sensor signal(s) generated by hardware sensor(s) integral with the computing device(s) may be detected and analyzed to determine an attribute of the user. Based on the analysis, the automated assistant may transition into an enhanced listening state in which the audio data may be monitored for enhanced hot word(s). Detection of enhanced hot word(s) triggers the automated assistant to perform a responsive action without requiring detection of default hot word(s).

Abstract: This disclosure relates to answer prediction in a speech processing system. The system may disambiguate entities spoken or implied in a request to initiate an action with respect to a target user. To initiate the action, the system may determine one or more parameters; for example, the target (e.g., a contact/recipient), a source (e.g., a caller/requesting user), and a network (voice over internet protocol (VOIP), cellular, video chat, etc.). Due to the privacy implications of initiating actions involving data transfers between parties, the system may apply a high threshold for a confidence associated with each parameter. Rather than ask multiple follow-up questions, which may frustrate the requesting user, the system may attempt to disambiguate or determine a parameter, and skip a question regarding the parameter if it can predict an answer with high confidence. The system can improve the customer experience while maintaining security for actions involving, for example, communications.

Abstract: A key word detection apparatus and a method for low-power voice-activated devices are presented. A first signal processing module operates with a first transducer to receive an incoming signal and generates a first sample. A second signal processing module operates with a second transducer which receives an incoming signal and generates a second sample. In summary, a signal processing system, in particular a key word detection system, has a first low power module that wakes up a second higher power module. The second module uses signals from the first module in order to improve accuracy of key word detection or other signal processing tasks.

Abstract: A computer-implemented method in accordance with one embodiment includes, in response to a submission of an input dataset to an artificially intelligent application, receiving an explanation from each module of the application. The modules are configured within the application in a serial sequence in which each module, upon receiving the input dataset and any input generated by an immediately preceding module of the serial sequence, generates output that is forwarded as input to a next module, if any, in the sequence. A determination is made that at least two of the received explanations are semantically inconsistent.

Abstract: Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.

Abstract: A speech recognition method including performing speech recognition on an inputted speech to obtain a first text, correcting the first text according to an obtained mapping relationship between words in different languages to obtain at least one second text, and in response to determining that the at least one second text corresponds to the same language, outputting the first text, or in response to determining that the at least one second text corresponds to different languages, determine an outputted text according to first probability values corresponding to each of the at least one second text. By combining the mapping relationships between words in different languages in correcting the initial ASR result, the present application ensures the accuracy of the final speech recognition result.

Abstract: Set forth is a motorized computing device that selectively navigates to a user according content of a spoken utterance directed at the motorized computing device. The motorized computing device can modify operations of one or more motors of the motorized computing device according to whether the user provided a spoken utterance while the one or more motors are operating. The motorized computing device can render content according to interactions between the user and an automated assistant. For instance, when automated assistant is requested to provide graphical content for the user, the motorized computing device can navigate to the user in order to present the content the user. However, in some implementations, when the user requests audio content, the motorized computing device can bypass navigating to the user when the motorized computing device is within a distance from the user for audibly rendering the audio content.

Abstract: Systems and methods for device functionality identification are disclosed. For example, a connected device may be coupled to a secondary device. A user may request operation of the connected device, and a system may determine that the connected device is of a given device type. Based on the connected device being of the given device type, the system may cause another device having an environmental sensor to send sensor data indicating environmental changes sensed by the sensor. The connected device may be operated and the sensor may sense environmental changes caused by operation of the connected device. When the sensed environmental changes indicate the device type of the secondary device, a recommendation to change the device type of the connected device to the device type of the secondary device may be provided to a user device associated with the connected device.

Abstract: According to an embodiment of the present invention, there is provided an artificial intelligence (AI) apparatus for mutually converting a text and a speech, including: a memory configured to store a plurality of Text-To-Speech (TTS) engines; and a processor configured to: obtain image data containing a text, determine a speech style corresponding to the text, generate a speech corresponding to the text by using a TTS engine corresponding to the determined speech style among the plurality of TTS engines, and output the generated speech.

Abstract: Techniques for implementing a chatbot that utilizes context embeddings are described. An exemplary method includes determining a next turn by: applying a language model to the utterance to determine a probability of a sequence of words, generating a context embedding for the utterance based at least on one or more of: a dialog act as defined by a chatbot definition of the chatbot, a topic vector identifying a domain of the chatbot, a previous chatbot response, and one or more slot options; performing neural language model rescoring using the determined probability of a sequence of words as a word embedding and the generated context embedding to predict an hypothesis; determining at least a name of a slot and type to be fulfilled based at least in part on the hypothesis and the chatbot definition; and determining a next turn based at least in part on the chatbot definition, any previous state, and the name of the slot and type to be fulfilled.

Abstract: Technology is described herein for generating questions using a neural network. The technology generates the questions in a three-step process. In the first step, the technology selects, using a first neural network, a subset of textual passages from an identified electronic document. In the second step, the technology generates, using a second neural network, one or more candidate answers for each textual passage selected by the first neural network, to produce a plurality of candidate passage-answer pairs. In the third step, the technology selects, using a third neural network, a subset of the plurality of candidate passage-answer pairs. The technology then generates an output result that includes one or more output questions chosen from the candidate passage-answer pairs produced by the third neural network. The use of the first neural network reduces the processing burden placed on the second and third neural networks. It also reduces latency.

Abstract: According to one embodiment, the interface-providing apparatus comprises an identifying unit and a generating unit. The identifying unit identifies a keyword from dialogue data including a question text to request information, and a response text in reply thereto. The generating unit generates display information to display a user interface for receiving feedback input relating to a degree of usefulness of a keyword when searching for the requested information.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing demographic-aware federated machine learning. In one example, a method includes receiving local machine learning model data objects from model data object provider agents; for each inference-profile pair that is associated with a corresponding inference identifier and a corresponding model profile, generating a global machine learning model data object based at least in part on a related local model subset of the local machine learning model data objects for the inference-profile pair; and providing, based at least in part on each global machine learning model data object, a demographic-aware predictive data analysis application programming interface (API), wherein the demographic-aware predictive data analysis API is accessible by the model data object provider agents.

Abstract: Messages are processed to generate effectiveness predictions and/or other insights associated with the messages. Candidate messages are processed through a natural language processing (NLP) component to parse the candidate message into message elements for further processing. The message elements are converted to a vector or set of vectors, which are provided as input to a machine learning model to make predictions of message effectiveness. A contribution score can be made for each message element of the candidate message, which may be indicative of the importance or relevance for the individual message element to the overall predicted message effectiveness. Other message elements not originally within the message can be provided as candidates to replace message elements already located within the message. In this way, a message that is likely to be effective, such being likely to have a high conversion rate, can be published or otherwise distributed.

Abstract: A method including embedding, by a trained issue MLM (machine learning model), a new natural language issue statement into an issue vector. An inner product of the issue vector with an actions matrix is calculated. The actions matrix includes centroid-vectors calculated using a clustering method from a second output of a trained action MLM which embedded prior actions expressed in natural language action statements taken as a result of prior natural issue statements. Calculating the inner product results in probabilities associated with the prior actions. Each of the probabilities represents a corresponding estimate that a corresponding prior action is relevant to the issue vector. A list of proposed actions relevant to the issue vector is generated by comparing the probabilities to a threshold value and selecting a subset of the prior actions with corresponding probabilities above the threshold. The list of proposed actions is transmitted to a user device.

Abstract: Provided are a method, system, and computer program product for representing text, in which a text is received and analyzed by utilizing a pre-trained embedding model and a feature vector model, wherein selected words in the text have corresponding weights. Operations whose parameters include weights of a feature vector and an embedding are performed to generate a weighted embedding data structure. A summation is performed of all corresponding columns of a plurality of rows of the weighted embedding data structure to generate a data structure that represents the text. The data structure that represents the text is utilized to generate at least one of a classification metadata for the text and a summarization of the text.

Abstract: A method of improving output content through iterative generation is provided. The method includes receiving a natural language input, obtaining user intention information based on the natural language input by using a natural language understanding (NLU) model, setting a target area in base content based on a first user input, determining input content based on the user intention information or a second user input, generating output content related to the base content based on the input content, the target area, and the user intention information by using a neural network (NN) model, generating a caption for the output content by using an image captioning model, calculating similarity between text of the natural language input and the generated output content, and iterating generation of the output content based on the similarity.

Abstract: An information processing system includes: a first device configured to acquire a user's uttered voice, transfer the user's uttered voice to at least one of a second and a third devices each actualizing a voice interaction agent, when a control command is acquired, convert a control signal based on the acquired control command to a control signal that matches the second device, and transmit the converted control signal to the second device; a second device configured to recognize the uttered voice transferred from the first device, and output, to the first device, a control command regarding a recognition result obtained by recognizing the uttered voice and response data based on the control signal; and a third device configured to recognize the uttered voice transferred from the first device, and output, to the first device, a control command regarding a recognition result obtained by recognizing the uttered voice.

Abstract: A system is provided for determining which component of a speech processing system is the cause of an undesired response to a user input. The system processes ASR data and NLU data to determine the component likely to cause the undesired response. Based on which component is the cause of the undesired response, the system performs an appropriate conversation recovery technique to confirm the speech processing results with the user.

Abstract: A transcription of audio speech included in electronic content associated with a meeting is created by an ASR model trained on speech-to-text data. The transcription is post-processed by modifying text included in the transcription, for example, by modifying punctuation, grammar, or formatting introduced by the ASR model and by changing or omitting one or more words that were included in both the audio speech and the transcription. After the transcription is post-processed, output based on the post-processed transcription is generated in the form of a meeting summary and/or template.